### **Background:**

In my role as a product manager and subject matter expert (SME), I played a crucial part in developing and launching HPE Ezmeral's key products: EzSQL, MLOps, and Apache Spark Managed Service. This case study outlines the practical contributions made to address common challenges faced by businesses in extracting meaningful insights from their data.

### **Business Challenge:**

Modern organizations encounter difficulties in managing vast datasets, navigating complex analytics infrastructures, and coping with a shortage of skilled personnel. These challenges hinder their ability to derive actionable insights and make informed decisions.

### **Expertise Utilized:**

Leveraging a deep understanding of data management, analytics, and machine learning, I led the development of Ezmeral's core products. This involved blending product management skills with SME insights to align technical features with practical business needs.

### **[Ezmeral Data Fabric Implementation:](https://www.hpe.com/in/en/hpe-ezmeral-data-fabric.html?jumpid=ps_myk2pu35k_aid-520074550&ef_id=Cj0KCQiAkeSsBhDUARIsAK3tiedy1OZVuhmXc5gIU3U37Wsh1wTi7G_NhN7JlBJtBjp_lsWGak5J390aAgYKEALw_wcB:G:s&s_kwcid=AL!13472!3!653370569178!e!!g!!hpe%20ezmeral%20data%20fabric!19918654965!147014945959&gad_source=1)**

I advocated for and contributed to the development of Ezmeral Data Fabric, a platform simplifying SQL access to diverse data sources across clouds and edges. This initiative aimed to transform complex data querying into a more intuitive experience, enabling users of varying skill levels to derive valuable insights.

### **[HPE MLOps:](https://www.hpe.com/in/en/solutions/ezmeral-machine-learning-operations.html)**

Recognizing the increasing demand for AI and ML, I played a central role in creating HPE Ezmeral's MLOps solution. This involved ensuring a streamlined end-to-end ML pipeline, covering data preparation, model training, deployment, and monitoring. The goal was to expedite ML initiatives and facilitate the adoption of AI technologies.

### **[Apache Spark Managed Service Implementation:](https://www.hpe.com/psnow/doc/a00118439enw)**

Understanding the significance of Apache Spark in big data analytics, I advocated for and supervised the development of a managed Spark service within Ezmeral. This initiative aimed to relieve organizations of infrastructure management burdens, granting immediate access to Spark's powerful analytics capabilities.

### **Practical Impact:**

Through these efforts, businesses experienced tangible benefits:

* **Unified Data Access:** Breaking down silos, enabling easy access to data from diverse sources for comprehensive insights.

* **Simplified Analytics:** Streamlining data querying and analysis, democratizing access to valuable information.

* **Accelerated AI/ML Adoption:** Simplifying the ML pipeline to make AI accessible to businesses of all sizes.

* **Operational Focus:** Reducing infrastructure management burdens, freeing up resources for strategic initiatives.

## **Legacy:**

The successful launch and adoption of HPE Ezmeral underscore the practical impact of these initiatives. By addressing real-world challenges, businesses can now extract better value from their data, make informed decisions, and navigate the complexities of the data-driven landscape. This case study highlights the role of a pragmatic product manager and SME in facilitating this transformation.

Building HPE Ezmeral

## **Business Insight at Cisco Catalyst Center with SAP HANA, SAP Data Services, and Tableau**

Cisco Catalyst Center, a leading force in networking solutions, sought to revolutionize its business insights. To address this challenge, a streamlined Extract, Transform, Load (ETL) pipeline was implemented on SAP HANA using SAP Data Services, further enhanced by the integration of Tableau for advanced data visualization.

### **Situation:**

Cisco Catalyst Center faced a bottleneck in deriving actionable insights due to fragmented data sources. The imperative was to consolidate and analyze data efficiently to support agile decision-making and strategic planning.

### **Complication:**

Traditional data integration methods proved sluggish and lacked the agility required for the dynamic business environment. Existing ETL processes needed optimization for speed and scalability.

### **Implementation:**

**1. SAP HANA Integration:**

* Leveraged SAP HANA's in-memory computing for real-time data processing.

* Established seamless connectivity between SAP HANA and existing data sources for a unified data landscape.

**2. SAP Data Services Deployment:**

* Implemented SAP Data Services for efficient ETL processes.

* Utilized SAP Data Services' data quality and transformation capabilities for streamlined data integration.

**3. Tableau Integration:**

* Integrated Tableau for advanced data visualization and analytics.

* Enabled Tableau to directly connect to SAP HANA, ensuring real-time visual insights.

### **Impact:**

The implementation of the streamlined ETL pipeline on SAP HANA using SAP Data Services, enriched by Tableau, resulted in significant impacts:

**1. Accelerated Data Processing:**

* SAP HANA's in-memory computing accelerated data processing.

* The streamlined ETL pipeline reduced data processing times, enabling real-time insights.

**2. Improved Data Quality:**

* SAP Data Services enhanced the accuracy and reliability of integrated data.

* Data cleansing and transformation processes were optimized for improved quality.

**3. Advanced Data Visualization:**

* Tableau's integration allowed for intuitive and interactive data visualization.

* Decision-makers gained a comprehensive understanding of data through dynamic dashboards.

### Technologies Used:

The success of this transformation can be attributed to the effective utilization of SAP HANA, SAP Data Services, and Tableau:

**1. SAP HANA:**

* In-memory computing for accelerated data processing.

* Real-time analytics capabilities.

**2. SAP Data Services:**

* Efficient ETL orchestration.

* Data quality and transformation features.

**3. Tableau:**

* Advanced data visualization.

* Real-time connectivity to SAP HANA for dynamic reporting.

### **Conclusion:**

The streamlined ETL pipeline on SAP HANA using SAP Data Services, complemented by Tableau's advanced data visualization, has positioned Cisco Catalyst Center for informed decision-making and strategic growth. By leveraging these powerful technologies, the organization is poised to thrive in the dynamic landscape of networking solutions.

Business Insight at Cisco Catalyst Center with SAP HANA, SAP Data Services, and Tableau

## **Background**

Noon is a popular online marketplace where multiple sellers list their products for buyers. However, having duplicate product listings from different sellers can result in a poor buying experience for users. The goal of this project is to implement a data-driven solution to identify and remove duplicate listings on the Noon marketplace.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FErCdt9kIm5TgenmgxPLK%2F5f5ffccf-912c-4654-a152-fb9d4d861c87.png?alt=media&token=e6292509-1ce0-4ae9-95fb-3229bfc2ed7f","id":"5f5ffccf-912c-4654-a152-fb9d4d861c87","width":704,"height":496,"filename":"Group 159.png","type":"image/png","caption":"Noon Duplicate Listing","border":false}]}
```

## **Problem statement**

One of the main challenges in this project is the large volume of data that needs to be processed to identify duplicate listings. Additionally, there can be variations in product attributes across different sellers, which can make it difficult to accurately identify duplicates. Another challenge is to ensure that all sellers receive equal opportunities to sell their products, while still removing duplicate listings.

## **Solution**

To address these challenges, we propose a combination of pre-creation and post-creation checks using machine learning algorithms. In the pre-creation stage, we can use product attributes such as SKU, product name, brand, model, etc. to identify potential duplicates before they are listed on the marketplace. If a potential duplicate is found, we can prompt the seller to list their product under an existing listing or provide more distinguishing [details.In](http://details.In) the post-creation stage, we can use supervised and unsupervised machine learning algorithms to analyze product attributes and seller information to identify duplicate listings that were not caught during the pre-creation stage. We can use clustering algorithms to group similar products based on product attributes and seller information. Based on the results, we can remove duplicate listings or merge them under a single listing. analysis.

**Results**The deliverables for this project include a machine learning model to identify potential duplicate listings before creation, periodic checks to identify duplicate listings that were not caught during the pre-creation stage, and a process for removing or merging duplicate listings. The effectiveness of the solution can be evaluated by measuring the percentage of duplicate listings that were removed from the marketplace and monitoring user feedback and satisfaction scores. The solution should ensure a fair and equal opportunity for all sellers while improving the buying experience for users on the Noon marketplace.

Removing Duplicate Listings on Noon Marketplace

## **Convert your intuition to evidence using Excel and Statistics**

In the world of product management and constant innovation, separating gut feelings from solid evidence is key to your success. This process is rooted in decision science, especially when it involves turning straightforward data into strategic decisions. It all begins with a hypothesis—a smart guess about how things might work. Before we dive deep into using statistics to test these hypotheses, let's first understand the basics. This is tailored for founders and product managers aiming to make more data-informed decisions in their roles.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F70f75bb7-a699-4c19-bbbc-3221c871d384.png?alt=media&token=3ded123b-03e3-4c0f-a2ee-dd0b4ea7cd63","id":"70f75bb7-a699-4c19-bbbc-3221c871d384","width":1920,"height":1080,"filename":"Frame 1.png","type":"image/png","caption":"","border":false}]}
```

## **What is a Product Hypothesis?**

A hypothesis in product development and product management is a **statement or assumption about the product, planned feature, market, or customer** (e.g., their needs, behavior, or expectations) **that you can put to the test, evaluate, and base your further decisions on**. It's crucial to note that a hypothesis arises from a position of limited knowledge and data, necessitating empirical testing to validate or refute your propositions.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F11842470-0fdc-4016-9d75-3ff9a93ec31d.png?alt=media&token=00b55f42-64fe-494c-8cf5-6d1df212ecba","id":"11842470-0fdc-4016-9d75-3ff9a93ec31d","width":1160,"height":2956,"filename":"Hypothesis Framework.png","type":"image/png","caption":"","border":false}]}
```

## **The Difference Between an Idea and a Hypothesis**

Understanding the distinction between an idea and a hypothesis is critical in the realm of product management.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2Fb404fb50-b44e-43e3-972b-65a997626f58.png?alt=media&token=ab20a2f6-bd4e-4cfc-8f7e-b3c7ea124317","id":"b404fb50-b44e-43e3-972b-65a997626f58","width":1342,"height":720,"filename":"Screenshot 2024-03-11 at 09.30.21.png","type":"image/png","caption":"","border":false}]}
```

Armed with a clear understanding of what constitutes a hypothesis, let's delve into the nuanced practice of hypothesis testing, where statistical rigor meets product creativity.

Armed with a clear understanding of what constitutes a hypothesis, let's delve into the nuanced practice of hypothesis testing, where statistical rigor meets product creativity.

### **The Core of Hypothesis Testing**

Hypothesis testing is a statistical method employed to ascertain the likelihood that a hypothesis regarding a product feature or user experience holds true. This process begins with the formulation of two hypotheses: the null hypothesis Ho and the alternative hypothesis H1

\- **Null Hypothesis Ho**: There is no effect or difference.

\- **Alternative Hypothesis H1**: There is a significant effect or difference.

### **Defining Success Metrics**

Success metrics quantitatively delineate the parameters of success, directly correlating to your hypothesis. For instance, the click-through rate (CTR) serves as a metric when testing the impact of button color changes.

### **Crafting and Conducting the Experiment**

Experimental design is pivotal, outlining the methodology for comparing variations and observing their impact. A/B testing is a common approach, where users are randomly assigned to experience different variations.

### **The Statistical Backbone: The Statistical tests and Beyond**

There are various statistical tests which are available for different use cases

1\. **T-Test:** Compare means between two groups.

2\. **Paired T-Test:** Compare means within the same group at different points.

3\. **ANOVA (Analysis of Variance):** Compare means among more than two groups.

4\. **Regression Analysis:** Examine the relationship between dependent and independent variables.

5\. **Correlation Analysis:** Measure the strength and direction of the linear relationship between two variables.

6\. **Cointegration Test:** Test if two time series are cointegrated, indicating a long-term relationship.

7\. **Stationarity Test:** Check if a time series is stationary.

8\. **Chi-Square Test:** Test the independence of categorical variables.

9\. **Jarque-Bera Test:** Test if a set of data follows a normal distribution.

10\. **Bootstrap Test:** Estimate the sampling distribution of a statistic through resampling.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2Fd1de1c9a-6b18-4e33-9255-773e5c00f751.png?alt=media&token=b1b51272-2704-4a2f-818b-ecfbb5277683","id":"d1de1c9a-6b18-4e33-9255-773e5c00f751","width":744,"height":325,"filename":"1710129329539.png","type":"image/png","caption":"","border":false}]}
```

## **Let's do a practical using our favourite tool "Excel"**

**Step 1: Define Your Hypothesis**

\- **Hypothesis**: Changing the "Contact Us" button color from green to blue will result in a higher click-through rate (CTR).

\- **Null Hypothesis**: Changing the "Contact Us" button color from green to blue will not result in a higher CTR.

This setup presumes that Variation B (blue button) is not inherently superior to Variation A (green button). The goal is to statistically prove whether Variation B enhances the CTR over Variation A.

**Step 2: Set Up Your Experiment**

\- Create two webpage versions: one with a green *"Contact Us"* button (Variation A) and another with a blue *"Contact Us"* button (Variation B).

\- Randomly divide website visitors into two groups to ensure unbiased exposure to each version.

\- Record the number of clicks on the *"Contact Us"* button for each group over a predetermined period, such as one week.

### Step 3: Get data

You can ask your data engineer or if you know a little about any tracking tool like GA4/ [Clarity](https://www.bing.com/webmasters/help/microsoft-clarity-55a30306)/ [Mixpanel](https://developer.mixpanel.com/reference/track-event) you can get this data from there if you've correctly configured the Tracker into your website/ tool

### Step 4:

After gathering data, we will perform t-test which is a powerful tool for evaluating the significance of differences between groups. The formula for the t-test is:

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F41e92648-7cdf-45ee-a569-4e2109040a31.png?alt=media&token=98a75073-d3ae-4497-b2f1-adfc07db699d","id":"41e92648-7cdf-45ee-a569-4e2109040a31","width":514,"height":95,"filename":"1710129329333.png","type":"image/png","caption":"","border":false}]}
```

* **t** is the t-statistic.

* **x1 bar** is the mean of the first sample.

* **x2 bar** is the mean of the second sample.

* **s1** is the variance of the first sample.

* **s2** is the variance of the second sample.

* **n1** is the number of observations in the first sample.

* **n2** is the number of observations in the second sample.

## The Data:

The data provides information on the performance of two variations of the website's "Contact Us" button (green and blue) during a specified date or time period.

1. **Date**: Time of data collection.

2. **Visitor Count**: Total users on the website during that time.

3. **GreenBtn\_Clicks\_Visitors:** Clicks on the green "Contact Us" button, showing engagement.

4. **BlueBtn\_Clicks\_Visitors:** Clicks on the blue "Contact Us" button, indicating engagement with the altered design.

5. **CTR (Click Through Rate)** = (Visitors with Button clicks (Green/ Blue))/Visitor count \*100

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F25d86340-00b1-4e3d-ad5b-e1df86fbb9f2.png?alt=media&token=a173cd34-c68f-4f1f-8d2b-db5a7d30988c","id":"25d86340-00b1-4e3d-ad5b-e1df86fbb9f2","width":1274,"height":866,"filename":"1710129329754.png","type":"image/png","caption":"","border":false}]}
```

Now we have to use excel's built in feature of data analysis for performing **"t-test for two sample assuming equal variance"** (check the below illustration)

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F568266ba-030a-459f-9fa4-aeb246a38172.png?alt=media&token=e7da1c06-5ad2-4323-a76b-e87c88c1731e","id":"568266ba-030a-459f-9fa4-aeb246a38172","width":965,"height":1000,"filename":"1710129330108.png","type":"image/png","caption":"","border":false}]}
```

The result will look something like this

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FFgjp2vuEwjr8vOyTN32I%2F1323da7b-099d-4cd4-be58-fb13af30b8ff.png?alt=media&token=601ded34-418c-4132-b170-62d60ccce4c0","id":"1323da7b-099d-4cd4-be58-fb13af30b8ff","width":744,"height":308,"filename":"1710129329832.png","type":"image/png","caption":"","border":false}]}
```

**In this P Value is 0.01455039 which is less than the significance level of 0.05**

Get the Excel sheet from here: <https://abhijit.objectstore.e2enetworks.net/hypothesis\_testing/website\_hypothesis\_testing.xls>

### Step-4: Identifying the insights

**How to read the results**

***The null hypothesis can be rejected, and it can be said that the change in button color had a statistically significant impact on the CTR if the p-value is less than the significance level (for example, 0.05). Thus, we cannot rule out the null hypothesis and conclude that the CTR was not significantly affected by the change in button color.***

This example shows that the test group's mean CTR is greater than the control group's (green button) and that the difference is statistically significant because the p-value from the T-Test is less than 0.05 with **95% Confidence Interval**. We can thus rule out the null hypothesis and conclude that the CTR was positively impacted by the **"Contact Us"** button's switch from green to blue.

So, the next time you find yourself scratching your head over a "unique" feature suggestion, you've got the perfect comeback: "Let’s see what the data says!" This approach transforms those "Hmm, are you sure?" moments into "Ah-ha! Let's test it" opportunities.

**Imagine this:** A stakeholder comes up with an idea that makes you wonder if they've been reading too much science fiction. Instead of a flat-out "No way," you can now say, "Interesting idea! Let's put it through our hypothesis testing process and see if the data backs it up." It's a win-win: you keep the peace, and who knows? Sometimes, the most out-there ideas turn out to be gold mines, as long as the data agrees.

Product Hypotheses testing using Excel

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FHoT1MCqYPEFqIOVmRM7P%2F40dee9f5-72fd-4d1f-81cb-d9f914546a87.jpg?alt=media&token=fbfff72e-9a67-47fa-b9cc-b1dbec3c5b05","id":"40dee9f5-72fd-4d1f-81cb-d9f914546a87","width":950,"height":633,"filename":"1710697875388.jpeg","type":"image/jpeg","caption":"","border":false}]}
```

The rapid advancement of Artificial Intelligence (AI) and the increasing size and capabilities of language models have created a pressing need for specialized hardware architectures designed specifically for AI workloads. Traditional GPUs, while effective for certain tasks, are no longer sufficient to keep up with the demands of cutting-edge AI applications.

Enter Groq and their groundbreaking Language Processing Unit (LPU). The LPU is a revolutionary computing architecture designed specifically for machine learning tasks. It offers performance gains that far surpass those of traditional GPUs, making it an ideal solution for the most demanding AI workloads.

Groq's Language Processing Unit (LPU) represents a paradigm shift in processor architecture, designed to revolutionize high-performance computing (HPC) and artificial intelligence (AI) workloads. This article will delve into the components, architecture, and workings of the LPU, highlighting its potential to transform the landscape of HPC and AI.

### Components and Architecture

The LPU's groundbreaking architecture consists of several key components:

**1. Processing Elements (PEs):** The LPU's processing power comes from its thousands of simple, identical processing elements (PEs). These PEs are organized in Single Instruction, Multiple Data (SIMD) arrays, enabling them to execute the same instruction on different data points concurrently.

**2. Centralized Control Unit (CU):** At the heart of the LPU lies a centralized control unit (CU) responsible for issuing instructions to the PEs and managing the flow of data and instructions. The CU ensures seamless communication between the PEs and memory hierarchy.

**3. Memory Hierarchy:** The LPU features a hierarchical memory structure, including a large on-chip SRAM, a high-bandwidth off-chip memory interface, and a sophisticated cache hierarchy. This memory hierarchy is optimized for high-bandwidth, low-latency data access.

**4. Network-on-Chip (NoC):** A high-bandwidth Network-on-Chip (NoC) interconnects the PEs, the CU, and the memory hierarchy. The NoC enables fast, efficient communication between different components of the LPU.

**5. Vector Processing:** The LPU supports vector processing, allowing it to execute multiple operations on large data sets simultaneously.

### How Groq's LPU Works

The LPU's unique architecture enables it to outperform traditional CPUs and GPUs in HPC and AI workloads. Here's a step-by-step breakdown of how the LPU works:

**1. Data Input:** Data is fed into the LPU, triggering the Centralized Control Unit to issue instructions to the Processing Elements (PEs).

**2. Massively Parallel Processing:** The PEs, organized in SIMD arrays, execute the same instruction on different data points concurrently, resulting in massively parallel processing.

**3. High-Bandwidth Memory Hierarchy:** The LPU's memory hierarchy, including on-chip SRAM and off-chip memory, ensures high-bandwidth, low-latency data access.

**4. Centralized Control Unit:** The Centralized Control Unit manages the flow of data and instructions, coordinating the execution of thousands of operations in a single clock cycle.

**5. Network-on-Chip (NoC):** A high-bandwidth Network-on-Chip (NoC) interconnects the PEs, the CU, and the memory hierarchy, enabling fast, efficient communication between different components of the LPU.

**6. Processing Elements:** The Processing Elements consist of Arithmetic Logic Units, Vector Units, and Scalar Units, executing operations on large data sets simultaneously.

**7. Data Output:** The LPU outputs data based on the computations performed by the Processing Elements.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FHoT1MCqYPEFqIOVmRM7P%2Fdfe6007f-46d4-414a-a5e6-39300f1db6c9.png?alt=media&token=4547d3da-3e7d-4f7f-9d97-7dacd057e68b","id":"dfe6007f-46d4-414a-a5e6-39300f1db6c9","width":1047,"height":1000,"filename":"Groq.png","type":"image/png","caption":"","border":false}]}
```

### Revolutionizing HPC and AI

The LPU's massively parallel architecture and high-bandwidth memory hierarchy enable it to excel in applications such as:

1\. Artificial Intelligence and Machine Learning: The LPU's ability to execute thousands of operations simultaneously makes it an ideal candidate for AI and ML applications, including deep learning, natural language processing, and computer vision tasks.

2\. High-Performance Computing: The LPU's scalable and flexible architecture allows it to tackle complex HPC workloads, such as scientific simulations, climate modeling, and molecular dynamics.

3\. Edge Computing: The LPU's energy-efficient design and compact form factor make it an attractive solution for edge computing devices, where power and space constraints are paramount.

### How LPU is different from GPU

**1. Architecture:**

```
- LPU: An LPU is designed specifically for natural language processing tasks, with a multi-stage pipeline that includes tokenization, parsing, semantic analysis, feature extraction, machine learning models, and inference/prediction.

- GPU: A GPU has a more complex architecture, consisting of multiple streaming multiprocessors (SMs) or compute units, each containing multiple CUDA cores or stream processors.
```

**2. Instruction Set:**

```
- LPU: The LPU's instruction set is optimized for natural language processing tasks, with support for tokenization, parsing, semantic analysis, and feature extraction.

- GPU: A GPU has a more general-purpose instruction set, designed for high-throughput, high-bandwidth data processing.
```

**3. Memory Hierarchy:**

```
- LPU: The LPU's memory hierarchy is optimized for natural language processing tasks, with a focus on efficient data access and processing.

- GPU: A GPU has a more complex memory hierarchy, including registers, shared memory, L1/L2 caches, and off-chip memory. The memory hierarchy in GPUs is designed for high-throughput, high-bandwidth data access, but may have higher latency compared to the LPU for specific NLP tasks.
```

**4. Power Efficiency and Performance:**

```
- LPU: The LPU is designed for high power efficiency and performance, with a focus on natural language processing tasks. It can deliver superior performance per watt compared to GPUs for specific NLP workloads.

- GPU: GPUs are designed for high throughput and performance, particularly for graphics rendering and parallel computations. However, they may consume more power than an LPU for the same NLP workload due to their more complex architecture and larger number of processing units.
```

**5. Applications:**

```
- LPU: The LPU is well-suited for natural language processing tasks, such as tokenization, parsing, semantic analysis, feature extraction, and machine learning model inference.

- GPU: GPUs are widely used in applications such as gaming, computer-aided design (CAD), scientific simulations, and machine learning. However, they are not optimized for natural language processing tasks, and an LPU would generally provide better performance and power efficiency for such tasks.
```

In summary, the LPU and GPU have different architectural designs and use cases. The LPU is designed specifically for natural language processing tasks, while GPUs are designed for high-throughput, high-bandwidth data processing, particularly for graphics rendering and parallel computations. The LPU offers a more streamlined, power-efficient architecture for natural language processing tasks, while GPUs provide a more complex, feature-rich architecture for a broader range of applications.

Groq's LPU: A Revolutionary Leap in Processing for High-Performance Computing and AI

**Executive Summary**

A prominent law firm sought a centralized repository of partner information, aggregated from multiple internal and external websites. Our team delivered a fully automated solution that:

1\. **Extracted Data** with a Python 3.9 + BeautifulSoup 4 web crawler, enriched with libraries like Requests and Selenium for dynamic content.

2\. **Transformed and Loaded** the data into a secure, scalable AWS-based infrastructure (S3, Glue, Redshift) via an automated CI/CD pipeline.

3\. **Built a Retrieval-Augmented Generation (RAG)** architecture leveraging LangChain, with vector embeddings stored in MongoDB’s VectorStore for advanced semantic querying.

This end-to-end system consolidated scattered partner data, unlocked complex NLP-driven queries, and significantly accelerated the firm’s research capabilities.

## **1. Business Context and Challenges**

### **1.1 Fragmented Data Ecosystem**

• **Diverse Sources**: Partner profiles resided on the firm’s site, industry directories, and legal ranking websites (e.g., Chambers, LexisNexis).

• **Low Data Quality**: Reliance on manual copying/pasting and ad-hoc spreadsheets increased data inconsistency and errors.

• **Complex Inquiries**: Existing search tools were limited to keyword matching, making it difficult to answer deeper queries like “Which partner has negotiated the largest arbitration in the energy sector?”

### **1.2 Strategic Objectives**

• **Centralized Data**: Consolidate partner information into a single “source of truth.”

• **Automated Pipeline**: Eliminate manual overhead through robust data ingestion, transformation, and storage processes.

• **Intelligent Search**: Leverage modern NLP (through Large Language Models) to enable advanced retrieval and summarization.

## **2. Technical Approach and Methodology**

### **2.1 Data Collection with Python 3.9, BeautifulSoup 4, and Selenium**

**2.1.1 Crawler Design**

• **Libraries and Frameworks**:

• **Requests** for straightforward GET/POST calls.

• **BeautifulSoup 4** to parse static HTML pages.

• **Selenium** for dynamic content extraction where JavaScript-driven elements are present (e.g., partner bios behind AJAX calls).

• **Modular Architecture**:

• **Config Files**: Each target website had a dedicated config specifying URL patterns, HTML selectors, and pagination rules.

• **Error Handling**: Implemented Python try-except blocks to handle broken links, timeouts, and captchas.

### **2.1.2 JSON Conversion**

• **Schema Definition**: Leveraged Python’s dataclasses or pydantic to enforce a consistent schema (e.g., name, position, specialty, bio, etc.).

• **Data Validation**: Used built-in validators to ensure mandatory fields (e.g., email, phone number) are not null and follow standard formats.

• **Output Storage**: Exported validated data to JSON, then stored locally before upload to AWS S3.

### **2.2 ETL Pipeline with AWS and Supporting Tools**

**2.2.1 Data Ingestion into Amazon S3**

• **S3 Bucket Configuration**:

• **Versioning** enabled for audit trails.

• **Lifecycle Policies** for transitions to infrequent access and archiving.

• **Security**: Employed AWS KMS for server-side encryption (SSE-KMS), restricting keys through IAM roles.

### **2.2.2 AWS Glue for Transformation**

• **Glue Crawlers**: Automated schema discovery for JSON, generating a Glue Data Catalog.

• **Glue ETL Jobs**: Written in PySpark to perform:

• **Cleansing**: Standardize date formats, unify naming conventions (e.g., “Senior Partner” vs. “Sr. Partner”).

• **Enrichment**: Cross-referenced external data (e.g., Chambers ranking) to add partner accolades.

• **Job Orchestration**: AWS Step Functions or Apache Airflow (running on Amazon MWAA) for scheduling and dependency management.

### **2.2.3 Loading into Amazon Redshift**

• **Redshift Provisioning**:

• **Cluster Type**: dc2.large or ra3 nodes for scalable compute and storage.

• **Subnet Groups** in a private VPC for secure data access.

• **Data Warehouse Schema**:

• **Star Schema**: Fact tables (e.g., “Partner\_Engagements”) referencing dimensional tables (“Dim\_PartnerInfo,” “Dim\_Specialty”).

• **Metadata Tracking**: Columns for last\_updated, source\_url for auditing.

• **Performance Optimization**:

• **Sort Keys and Dist Keys** to minimize data movement for heavy queries.

• **Column Encoding** chosen by Redshift’s ANALYZE COMPRESSION function.

### **2.3 CI/CD and DevOps**

• **Version Control**: GitLab for code repositories (crawler scripts, Glue ETL jobs, and Redshift schema definitions).

• **Continuous Integration**: GitLab CI jobs running lint checks (Flake8, Black) and unit tests.

• **Docker Containerization**: Packaged the crawler code into Docker images stored on AWS ECR for reproducible runs.

• **Infrastructure as Code**: Deployed S3 buckets, Glue jobs, and Redshift clusters via AWS CloudFormation or Terraform.

### **2.4 Building an NLP-Powered Search Engine (RAG) with LangChain**

**2.4.1 Vector Embedding Generation**

• **LangChain Integration**:

• **Language Model**: Hugging Face Transformers (e.g., sentence-transformers/all-MiniLM-L6-v2) for embedding generation.

• **Embedding Pipelines**: Batch processed partner bios and specialized legal documents.

• **Data Flow**:

1\. Query Redshift for partner data.

2\. Use LangChain to transform text fields into vector embeddings.

3\. Store embeddings in MongoDB’s VectorStore.

### **2.4.2 MongoDB VectorStore**

• **Schema**:

• \_id: Unique partner identifier or doc ID.

• embedding: High-dimensional float array.

• metadata: Additional fields (e.g., partner name, practice area).

• **Similarity Search**:

• **Cosine Similarity**: Implemented within the VectorStore to find nearest neighbors in embedding space.

• **Indexing**: Utilized MongoDB Atlas Search for indexing vectors and accelerating queries.

### **2.4.3 RAG Query Flow**

1\. **User Query**: Input captured via a React/Next.js web front-end or a chatbot interface built in Streamlit.

2\. **LangChain Orchestration**:

• **Retrieval**: Identifies top-k relevant partner embeddings from MongoDB.

• **Contextual Assembly**: Aggregates relevant partner details.

• **LLM Response**: Passes context to the LLM (OpenAI GPT-4 or local LLM) to generate a final, succinct answer.

3\. **Answer Delivery**: Renders a final text response along with relevant partner profiles.

## **3. Advanced Architecture Diagram** 

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FLXigo2EazpxHDf4B5sAs%2Fcdf64db4-910c-4673-ade8-4ada1fee29f3.png?alt=media&token=ac4c165d-d4ca-44b7-8271-7921b2bf5e3a","id":"cdf64db4-910c-4673-ade8-4ada1fee29f3","width":1583,"height":3840,"filename":"Untitled diagram-2024-12-26-124544.png","type":"image/png","caption":"","border":false}]}
```

1\. **Crawler** uses Python + Selenium to handle dynamic sites.

2\. **S3** houses raw JSON data, secured and versioned.

3\. **Glue** orchestrates ETL: auto-discovery, cleaning, and loading.

4\. **Redshift** acts as the central data warehouse for analytics.

5\. **LangChain + Embeddings** pipeline transforms partner text data into vector forms.

6\. **MongoDB VectorStore** enables fast semantic search.

7\. **LangChain RAG** provides context-aware retrieval and LLM-driven answers.

8\. **UI/Chatbot** is the front-end portal for end-users.

### **4. Results and Key Metrics**

1\. **Data Quality Improvement**: Null and duplicate records reduced by **90%** through schema validation and automated pipeline checks.

2\. **Operational Efficiency**: The law firm saved an estimated **800 person-hours** per quarter by retiring manual data collection processes.

3\. **Enhanced Query Capabilities**: Complex, multi-faceted queries (e.g., “List partners with arbitration experience in the APAC region”) are now answered in **under 2 seconds** via the RAG-based approach.

4\. **Scalability**: The combination of AWS Glue and Redshift ensures easy horizontal scaling, accommodating thousands of new partner records and documents per day.

Law Firm Partner Intelligence Platform: End-to-End State Diagram

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FSnq8kfXcSqiXMtS02aBz%2F6c9c2738-daf2-440d-b22b-a698227a7d29.png?alt=media&token=9e3d33a7-9119-474f-bf3a-7166e8c2e9c7","id":"6c9c2738-daf2-440d-b22b-a698227a7d29","width":1910,"height":1156,"filename":"Screenshot 2024-05-10 at 23.23.50.png","type":"image/png","caption":"","border":false}]}
```

## **Statistical Arbitrage: A Dive into Quantitative Finance Strategies**

Statistical arbitrage emerges as a powerhouse in the trading world, leveraging mathematical finesse and high-speed technology to tap into the subtle inefficiencies between financial instruments. It’s not just about buying low and selling high; it's an orchestral play of numbers, predictions, and precision. Here's how it unfolds in the labyrinth of the financial markets.

### **The Essence of Statistical Arbitrage**

At its core, statistical arbitrage is about playing the symphony of numbers across different instruments. This strategy is broadly split into two main approaches:

* **Directional Trading**: This straightforward method focuses on the movement of a single financial instrument. Simple yet risky, it's akin to betting all your chips on one number at the roulette table.

* **Pairs and Cointegration Trading**: Here lies the art of balancing. Traders pick pairs or triplets of assets whose values historically move in sync. It's like a dance between assets, where if one stumbles, the others are expected to follow.

### **The Science of Stationarity and Its Role in Trading**

Stationarity is the backbone of statistical arbitrage. For a process to be stationary, its mean, variance, and covariance need to remain constant over time. Unlike a random walk, which wanders aimlessly, stationary processes have predictable patterns, making them ideal candidates for trading strategies like mean reversion.

### **Tools of the Trade: Decoding Stationarity**

1. **Augmented Dickey Fuller Test (ADF)**: This statistical test is like a litmus test for predictability in financial data. It helps determine whether a price series is more likely to revert to a mean or continue on a wild, unpredictable path.

2. **Hurst Exponent**: This is your compass in the realm of financial time series. A Hurst exponent above 0.5 signals a trending market, while a value below 0.5 indicates a potential for mean reversion.

### **Crafting a Mean Reversion Strategy on Stationary Series**

When two assets have danced together historically, mean reversion strategy helps predict when they'll return to their synchronized steps after a misstep. Here’s how it works:

1. **Calculate the Hedge Ratio**: This involves using Ordinary Least Squares (OLS) to determine how much of one asset (say, stock A) you should hold against another (stock B) to balance their differing volatilities.

2. **Creating the Spread**: Using the first 90 days' data, traders create a 'spread' between the two assets, ensuring no look-ahead bias, a tricky but vital part of the strategy.

3. **Testing for Cointegration**: Finally, an ADF test on the spread helps confirm if the assets are indeed moving together over the long term, justifying the use of mean reversion.

### **Putting Theory into Practice: A Step-by-Step Guide**

#### **Implementing the Hurst Exponent Test**

1. **Gather Data**: Collect the sequential data for analysis.

2. **Prepare and Preprocess**: Ensure your data is clean and formatted for time series analysis.

3. **Divide and Conquer**: Break the data into smaller series of varying lengths.

4. **Calculate and Plot**: Calculate the R/S statistic for each series and plot these on a log-log scale.

5. **Fit and Calculate**: Fit a line through the plot to estimate the Hurst Exponent.

6. **Analyze and Interpret**: A Hurst Exponent above 0.5 suggests persistence; below 0.5 suggests anti-persistence.

#### **Conducting an Augmented Dickey Fuller Test**

1. **Understand the Data's Nature**: Knowing whether your data is predictable (stationary) or not (non-stationary) is crucial.

2. **Set Up Hypotheses**: Null hypothesis assumes non-stationarity; the alternative suggests stationarity.

3. **Perform the Test**: Use the ADF statistic to challenge the null hypothesis.

4. **Decision Time**: Compare the statistic with critical values to decide the nature of your data.

5. **Interpret Results**: A rejection of the null hypothesis confirms stationarity, paving the way for predictive strategies.

Statistical arbitrage isn't just a strategy; it's a high-stakes game of precision and prediction. By understanding the nuances of stationarity, employing rigorous tests like the ADF and Hurst Exponent, and meticulously crafting trading strategies based on these insights, traders can harness the power of quantitative finance to not just participate in the market but to anticipate its moves. Remember, in the world of trading, being forearmed with data is being forewarned about opportunities.

Check the code implementation here \
https://github.com/abhi647/statistical-arbitrage

Statistical Arbitrage: A Dive into Quantitative Finance Strategies

## **Maximizing Revenue with RMF Analysis: A Salesforce and Einstein Case Study**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FTQxOymwmambcqmxJZECL%2Fa1523dc5-e682-4227-b526-6c9ef4551aba.png?alt=media&token=33a322f5-69d6-4fce-b61d-be2bc92d8b02","id":"a1523dc5-e682-4227-b526-6c9ef4551aba","width":1439,"height":1344,"filename":"RMF.png","type":"image/png","caption":"","border":false}]}
```

### **Background**

ABC Apparel is an e-commerce company that specializes in selling clothing and accessories online. The company operates through its online platform and uses Salesforce as its primary CRM system to manage customer data and sales activities. I was hired as a data analyst to conduct a recency, frequency, monetary (RFM) analysis to segment ABC Apparel's customer base and gain insights into their purchase behaviors.

### **Objective**

The main objective of the project was to identify ABC Apparel's most valuable customers and improve the company's customer retention and acquisition strategies. By conducting an RFM analysis, I aimed to segment the customer base based on purchase behaviors and identify patterns and trends to gain insights into customer preferences and behavior.

### **Solution**

The RMF analysis was conducted in several phases, including the following:Categorization: The first step was to identify the e-commerce company's assets and categorize them based on their importance and impact on the business. The assets included the Salesforce CRM system, Einstein for AI-powered analytics, and Tableau for data visualization.Threat identification: The second step was to identify the potential cybersecurity threats that could affect the company's assets. The threats identified included data breaches, malware infections, phishing attacks, and insider threats.Risk assessment: The third step was to assess the risks associated with each threat identified in the previous step. The assessment considered the likelihood of the threat occurring, the impact it would have on the business, and the existing controls in place to mitigate the risk.Risk mitigation: Based on the results of the risk assessment, the fourth step was to develop a risk mitigation plan. This plan included recommendations for implementing additional controls to reduce the likelihood and impact of the identified risks.

### **Methodology**

To carry out the RFM analysis, I gathered customer information, purchase history, and order details from Salesforce and Einstein. I used Tableau as my data visualization tool to create interactive dashboards to visualize and analyze the data. Using the RFM model, I segmented ABC Apparel's customer base into four groups based on their purchase behaviors:

1. **High-Value Customers:** Customers who made purchases recently, frequently, and spent a significant amount of money on each order.

2. **Mid-Value Customers:** Customers who made purchases within the last six months, spent an average amount of money on each order, and made purchases at a moderate frequency.

3. **Low-Value Customers:** Customers who made purchases over six months ago, spent a small amount of money on each order, and made infrequent purchases.

4. **Lost Customers:** Customers who have not made any purchases in the last year.

### **Results**

1. The data lake and reporting architecture have enabled Boeing to streamline data access, improve data quality, and gain insights into the product development process. The benefits include:

2. **Improved data quality:** The centralized data lake ensures consistent and accurate data across the enterprise. The data quality is improved by identifying and fixing data issues during the data transformation process.

3. **Faster data access:** The data lake enables faster access to data by reducing data latency and improving query performance. The data can be queried using different reporting tools, and the results are returned in seconds.

4. **Cost savings:** The data lake and reporting architecture have reduced the cost of maintaining and managing the CAD and PLM data. The cloud-based architecture provides a scalable and cost-effective solution that reduces the need for on-premise infrastructure.

5. Insights into the product development process: The data analytics capabilities have enabled Boeing to gain insights into the product development process. The analytics includes trend analysis, root cause analysis, and predictive modeling. These insights help the company to improve its design, manufacturing, and testing processes.

### **Conclusion**

The RFM analysis provided ABC Apparel with valuable insights into its customer base's purchase behaviors and helped the company identify its most valuable customers. By focusing on its High-Value Customers, ABC Apparel was able to improve customer retention and acquisition strategies and increase its revenue. The success of the project demonstrated the importance of data analysis in business decision-making and showcased my skills as a data analyst.

Maximizing Revenue with RMF Analysis: A Salesforce and Einstein Case Study

**Problem Statement: A leading online retailer faces a sudden dip in profitability for a $999 product at the beginning of 2022, despite a consistent trend in 2021. Our analysis aims to uncover the reasons behind this decline.**

**Key data points include:**

1. Shopping Event (Binary): Indicates special sales events.

2. Ad Spend (Numeric): Investment in advertising campaigns.

3. Page Views (Numeric): Visits to the product detail page.

4. Unit Price (Numeric): Price, factoring in temporary discounts.

5. Sold Units (Numeric): Quantity of units sold.

6. Revenue (Numeric): Daily sales revenue.

7. Operational Cost (Numeric): Daily operational expenses.

8. Profit (Numeric): Daily net profit.

### **Review Data:**

* Shape: 455 rows and 9 columns

* Null Values: There is no null value in the dataset

* Columns : By unique values we came to know that there is one categorical column ie: Shopping Event? rest of the columns have continuous data

* Data Type : Date and shopping events are object and boolean data types which can not be fed to the learning algorithm so they are required to be transformed.

* head: date column needs to be divided into day, month and year.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F426d993f-e40b-4b3a-a5e3-7ddc54a93e86.png?alt=media&token=e652c6d4-014c-4415-a597-814dbffd69f2","id":"426d993f-e40b-4b3a-a5e3-7ddc54a93e86","width":1200,"height":361,"filename":"Screenshot 2024-08-23 at 10.06.52.png","type":"image/png","caption":"","border":false}]}
```

### **Exploratory Data analytics**

**In exploratory data analytics we are expected to know reason behind the dip in 2022**

**To visualize the data against the date we have extracted the year and month from the date field and also changed the data type of the date column from object to datetime by using pandas to\_datetime method and extracted the year and month by using pandas DatetimeIndex method.**

#### **Profit Vs Date:**

By visualizing profit viruses date we can see if there is actually a dip in profit in 2022 and visualize the strength as well. We will be using the seaborn library to visualize the data.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Fc072619e-ed3e-4d08-b99e-f81c638a654c.png?alt=media&token=79e448ca-d1d9-4f40-bde1-cd18be2473fc","id":"c072619e-ed3e-4d08-b99e-f81c638a654c","width":1217,"height":752,"filename":"Screenshot 2024-08-23 at 10.07.41.png","type":"image/png","caption":"","border":false}]}
```

**In plot 2 we can clearly see the dip in profit as the year passes. By plot 4 we can see that there is a churn in the customer as compared to fiscal year 2021.**

**Continuous Variables Vs Profit**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F4a0966e9-4fd1-44be-b0ff-193a3c273575.png?alt=media&token=cb8ce31e-8cc7-42f9-937f-82cfd3456ef5","id":"4a0966e9-4fd1-44be-b0ff-193a3c273575","width":1153,"height":578,"filename":"Screenshot 2024-08-23 at 10.08.18.png","type":"image/png","caption":"","border":false}]}
```

### **Categorical variable Vs Profit**

**I have used a violin graph to visualize the categorical feature ie: *shopping event? Vs profit,* i have also marked the divergence in the graph as well.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Fb19ba143-9e81-453a-95d3-366b26cbaa66.png?alt=media&token=8f8d6020-edb3-4d09-8d15-95b7e56a337e","id":"b19ba143-9e81-453a-95d3-366b26cbaa66","width":1010,"height":664,"filename":"Screenshot 2024-08-23 at 10.08.59.png","type":"image/png","caption":"","border":false}]}
```

**Here the divergence the the year 2021 to 2022 is not very large though the causal effect might be more that we will calculate further in this document.**

### **Date Vs Categorical & Continuous Variables**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Faa39375d-6590-4b9d-93b6-bb7530dd285d.png?alt=media&token=23acb794-e1bc-4fc9-9e22-bf2fabc2c214","id":"aa39375d-6590-4b9d-93b6-bb7530dd285d","width":1208,"height":612,"filename":"Screenshot 2024-08-23 at 10.09.29.png","type":"image/png","caption":"","border":false}]}
```

* **Plot 1: Avg. 'Ad spend' is relatively the same for both the year but there is more in July 2022 which increases revenue for the same month of the same year.**

* **Plot 2: Avg. 'Page view' is very low in 2022 which might be the main culprit behind the decrease in profit.**

* **Plot 3: Surprisingly avg unit sold per indices per month is more in 2022 dispite low profit**

* **Plot 5: Operational cost is high is 2022**

* **Plot 6: unit price is very low in 2022**

#### **Correlation map**

1. **Pearson correlation defines that there is a strong relation between all the  variables, but we are interested to see an exclusive relationships so we can telly kendall tau and spearman correlation which can give the direction of the relationship as well.**

2. **By Concluding kendall tau and spearman correlation we noticed below relations 2.1 page views and profit have positive relation**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Fba503b63-6f62-49fe-9bf5-61a341db04f9.png?alt=media&token=a81f6a95-9f8f-4ad1-bcbf-e86ccb223b9c","id":"ba503b63-6f62-49fe-9bf5-61a341db04f9","width":1213,"height":1132,"filename":"Screenshot 2024-08-23 at 10.11.31.png","type":"image/png","caption":"","border":false}]}
```

#### **Conclusion for EDA**

**We can conclude from above EDA that page views have a definite correlation that might be affecting the profit in 2022. We also saw strong correlation for page view and profit in spearman correlation**

\
**Causal Inference mathematical approach**

**It is the field of data science that aims to quantify the cause and effect relationship between the variables.**

I am calculating causal effect by mean of ITE ie: individual term frequency. 

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Fcf900f41-7df4-4601-bbfc-5c2ea6f7da48.png?alt=media&token=26c13da8-3f58-4b87-812b-81cba2222dcc","id":"cf900f41-7df4-4601-bbfc-5c2ea6f7da48","width":988,"height":430,"filename":"Screenshot 2024-08-23 at 10.12.39.png","type":"image/png","caption":"","border":false}]}
```

**Ref. Article: [Recent Developments in Causal Inference and Machine Learning](https://www.degruyter.com/document/doi/10.1515/jci-2021-0025/html?lang=en)** 

**I am using pandas dataframe for loading data and and numpy for calculation let's look at the code snippet**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F304f1053-f72c-4431-bfec-842aca2988c7.png?alt=media&token=7c98f815-30e6-4ac5-a7dd-a6712b132cb4","id":"304f1053-f72c-4431-bfec-842aca2988c7","width":1025,"height":641,"filename":"Screenshot 2024-08-23 at 10.13.26.png","type":"image/png","caption":"","border":false}]}
```

**By using the seaborn library i am plotting bar graph for causal effect of all the columns that we have calculated using above calculation.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F8a285aa8-516f-4313-8cb7-e4a503610022.png?alt=media&token=23172235-3225-4f57-993a-0ff13896c1d7","id":"8a285aa8-516f-4313-8cb7-e4a503610022","width":763,"height":580,"filename":"Screenshot 2024-08-23 at 10.13.48.png","type":"image/png","caption":"","border":false}]}
```

**causal effect for shopping event is $12,68,282. which means when the shopping  event is true it causes the profit to increase by $12,68,282. So the treatment is  associated with increase in profit by coefficient 1268282.**

### **Root Cause Analysis using mathematical approach**

**I am using Kullback-Leibler Divergence for calculating the divergence between the data for fiscal year 2021 and 2022.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Ff9dc35a4-3129-4b7c-b7d8-d8aa63e7a397.png?alt=media&token=cac4ebe5-7e50-4918-bab1-20f8d39d4f60","id":"f9dc35a4-3129-4b7c-b7d8-d8aa63e7a397","width":967,"height":389,"filename":"Screenshot 2024-08-23 at 10.14.42.png","type":"image/png","caption":"","border":false}]}
```

### **Output**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F76983b3a-65c2-420d-ba7b-57c40cc83ec8.png?alt=media&token=52156ea5-5fd8-4bd8-bbaf-1a88c833c015","id":"76983b3a-65c2-420d-ba7b-57c40cc83ec8","width":893,"height":450,"filename":"Screenshot 2024-08-23 at 10.15.09.png","type":"image/png","caption":"","border":false}]}
```

**By using KL divergence we found top three root factors ie: Operational cost, Revenue, Page Views.**

### **Causal Effect and Root Cause analysis using Dowhy**

Dowhy is an open source Python library that aims to spark causal thinking and analysis. DoWhy provides a principled four-step interface for causal inference that focuses on explicitly modeling causal assumptions and validating them as much as possible.

In Dowhy the first step is to provide a causal relationship to the dowhy model. We have to provide the causal relation by our own experience. We have to pack the relation in networksx object container and then pass it to the model. Let's look at the code snippet.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F36b3f534-8213-4cd8-9194-a195df0ae709.png?alt=media&token=aee5157c-7733-418d-a9e3-982e46172381","id":"36b3f534-8213-4cd8-9194-a195df0ae709","width":548,"height":359,"filename":"Screenshot 2024-08-23 at 10.15.40.png","type":"image/png","caption":"","border":false}]}
```

**We can also plot the relation as well.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F21cc6adc-cb43-45b3-afcd-678a64e612a4.png?alt=media&token=64780958-2bc3-4ebe-b2ed-467ed491bc4d","id":"21cc6adc-cb43-45b3-afcd-678a64e612a4","width":969,"height":658,"filename":"Screenshot 2024-08-23 at 10.16.07.png","type":"image/png","caption":"","border":false}]}
```

**Next step is to fit the data into the model.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F14dbbfff-e38b-42bc-8483-25b7a2e42cf5.png?alt=media&token=4f1baf71-d89a-433c-9caa-34c40f0dd04e","id":"14dbbfff-e38b-42bc-8483-25b7a2e42cf5","width":1065,"height":151,"filename":"Screenshot 2024-08-23 at 10.16.29.png","type":"image/png","caption":"","border":false}]}
```

### **Causal effect using Dowhy**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2Ff1a0916e-12f0-4c9c-a269-a8420a914332.png?alt=media&token=25b42045-3626-4af1-9a02-55ff47e7079a","id":"f1a0916e-12f0-4c9c-a269-a8420a914332","width":1085,"height":680,"filename":"Screenshot 2024-08-23 at 10.17.05.png","type":"image/png","caption":"","border":false}]}
```

#### **Root Cause Analysis using Dowhy**

**For calculating the root cause we use attribute\_anomalies method of Dowhy. This require an input of a causal graph which we had already added while finding causal effect.**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F9e12180c-c907-4452-bce9-5126c825ef84.png?alt=media&token=2be08a37-6e9b-4127-b338-995f72cf23b9","id":"9e12180c-c907-4452-bce9-5126c825ef84","width":1015,"height":132,"filename":"Screenshot 2024-08-23 at 10.17.34.png","type":"image/png","caption":"","border":false}]}
```

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FWrxyEZ7RVg7iDSZ57Wb2%2F2995ffe1-3348-4ec7-a7fd-b151d9e0c5a4.png?alt=media&token=a9d8feea-aadc-4741-b566-7afd53296ffd","id":"2995ffe1-3348-4ec7-a7fd-b151d9e0c5a4","width":1040,"height":779,"filename":"Screenshot 2024-08-23 at 10.17.51.png","type":"image/png","caption":"","border":false}]}
```

### **Conclusion**

**In this article, we have  compared root cause analysis and causal inference by classical approach, mathematical approach and by using Dowhy library. In the classical approach we have using exploratory data analytics, in the mathematical approach we computed causal effect and divergence/ root cause using mathematical formulas, then we used dowhy open source library to compute the same ie: causal effect and root cause.** 

**The classical and mathematical approach give better understanding of the data whereas in dowhy it is very convenient to find the result but in dowhy we are not able to understand the backend calculation. Dowhy is a bit more computational whereas the mathematical approach is simple and less computational**

### **Potential Solution**

The decline in page views led to a drop in sales, and the decline in unit pricing had a detrimental effect on revenue as well. Let's talk about some of the possible fixes that may be put into practice for increasing page views.

1. Optimize your website for organic search: Search is the top way both individuals and businesses research new products and services. This means it’s critical for you to make sure search engines find your website and bring it to the attention of the right people through search engine optimization (SEO). 

2. Invest in paid search

3. Engage in social channels

4. Work with influencers: Find social influencers and bloggers who have sizable audiences in your target demographic. Posts shared by influencers can help boost awareness and your SEO value if they feature your products in an authentic way. 

5. Write blogs or articles: Publishing original content, either on your own blog or on industry websites, can position you as a thought leader. 

6. Drive awareness with public relations (PR): There are plenty of ways to do PR on a budget, either on your own or with the help of a small agency or freelancer. Local publications and websites are always looking for interesting stories and contacting their editors with a pitch can lead to wonderful visibility that will attract high-potential website traffic. 

7. Use retargeting display ads: Retargeting can capture the attention of customers who visit your website but leave without making a purchase.

8. Make the most of email: Email is still the preferred method of communication for many buyers, and it can increase traffic from your existing audience. Emails that connect at different points in the customer journey can be automated with minimal effort, freeing you up for other important tasks. These include welcome emails that introduce new customers to your brand, abandoned cart emails to bring people back to complete a purchase, messages highlighting best-selling items, and more.

Root Cause Analysis

## **Voice Up, Vision Forward: Decoding the Future of Multimodal Tech**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FZ7ZG3gUbYLSk71C5i8u2%2F803bcf84-0208-4b73-a2ef-ecdbc911629e.jpg?alt=media&token=a7595b1b-6c08-4c9c-b9f1-29e6b51f9441","id":"803bcf84-0208-4b73-a2ef-ecdbc911629e","width":1892,"height":1116,"filename":"IMG_0043.jpg","type":"image/jpeg","caption":"","border":false}]}
```

As we embark on 2024, the echoes of technological marvels from the past year still resonate. In the realm of voice computing, advancements like Meta Glasses and the Humane AI Pin have pushed the boundaries of what's possible. They whisper promises of a future where our voices seamlessly orchestrate our digital interactions.

But beneath this optimism lies a paradox. While these devices showcase the potential of voice, challenges remain in establishing it as a standalone category. Consider the saga of Amazon's Alexa, a household name that still incurred a staggering $10 billion loss. This begs the question: are we struggling with monetization, or is there a deeper flaw in isolating voice as a tech force?

## The Current Landscape: Beyond the Hype

Let's peel back the layers. Devices like Meta Glasses and the Humane AI Pin, equipped with generative AI, represent the pinnacle of voice capabilities. But challenges lurk in the shadows. Dependence on visual interfaces for setup, privacy concerns, accuracy issues, social discomfort, and limited multimodal integration exposes the limitations of a voice-only experience.

Market trends and user behaviors add another layer of complexity. Studies reveal a preference for a hybrid approach, where voice and visual interfaces work in tandem. Users instinctively switch back to traditional interfaces in certain scenarios, highlighting the need for holistic solutions.

## Navigating the Future: Voice as an Enhancer, Not a Replacement

As we gaze toward the horizon, let's approach voice computing with nuance. While Meta Glasses, Humane AI Pin, Apple's AirPods Pro+, Google's Pixel Lens, and Microsoft's HoloVoice showcase its potential, they also reveal its true nature: a powerful accessory, not a replacement for visual interfaces.

Designers and product managers, armed with this knowledge, can pioneer solutions that leverage voice's strengths while embracing its symbiotic relationship with visual interfaces. In doing so, we can create tech solutions that resonate with users and stand the test of time.

**What are your thoughts?** Are we on the brink of a voice-powered revolution, or is its true potential as an enhancer, not a replacement? Share your insights and aspirations for the tech landscape in the comments below!  #VoiceComputing2024 #TechInnovation #NewYearTech #FutureTechTrends

P.S. Don't forget to check out the Humane AI Pin for a glimpse into the future of voice-driven experiences!

Voice Up, Vision Forward: Decoding the Future of Multimodal Tech

### **Background**

Boeing is an American multinational corporation that designs, manufactures, and sells airplanes, rotorcraft, rockets, and satellites. With over 150,000 employees and operations in more than 65 countries, it is one of the largest aerospace companies in the world.

### **Problem statement**

Boeing's Computer Aided Design (CAD) and Product Lifecycle Management (PLM) data are distributed across different systems and teams. This fragmentation leads to inefficient data access, low data quality, and increased costs in maintenance and management. Boeing wants to streamline data access, improve data quality, and enable the use of data analytics to gain insights into the product development process.

### **Solution**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Fboeing%2Fb83f2dbf-a523-4712-aa24-acea9dd21e37.png?alt=media&token=1e6a374f-b8bb-4c47-84c9-e5fd92f6cf46","id":"b83f2dbf-a523-4712-aa24-acea9dd21e37","width":1120,"height":413,"filename":"Untitled.png","type":"image/png","caption":"Solution Architecture for Boeing Solution","border":false}]}
```

To address the problem, I built a data lake and reporting architecture for Boeing's CAD and PLM data. The data lake is a centralized repository that consolidates the data from various systems and teams. The data is stored in a scalable, fault-tolerant, and cost-effective manner using Amazon Web Services (AWS) cloud technologies, such as Amazon S3 and Amazon Redshift.

We used AWS Glue to automate the data ingestion and transformation process. The data is extracted from the source systems using AWS Glue connectors, cleaned, enriched, and transformed using Python and Spark scripts and loaded into the data lake. The data is partitioned by date and other relevant attributes to enable faster query performance.We designed a reporting architecture that enables users to access the data lake using different reporting tools, such as Tableau, Power BI, and QuickSight. The reporting architecture is built on top of AWS Redshift, a data warehousing solution that supports fast query performance, concurrency, and scalability. The data is modeled using a star schema to enable efficient aggregation and analysis.

### **Results**

1. The data lake and reporting architecture have enabled Boeing to streamline data access, improve data quality, and gain insights into the product development process. The benefits include:

2. Improved data quality: The centralized data lake ensures consistent and accurate data across the enterprise. The data quality is improved by identifying and fixing data issues during the data transformation process.

3. Faster data access: The data lake enables faster access to data by reducing data latency and improving query performance. The data can be queried using different reporting tools, and the results are returned in seconds.

4. Cost savings: The data lake and reporting architecture have reduced the cost of maintaining and managing the CAD and PLM data. The cloud-based architecture provides a scalable and cost-effective solution that reduces the need for on-premise infrastructure.

5. Insights into the product development process: The data analytics capabilities have enabled Boeing to gain insights into the product development process. The analytics include trend analysis, root cause analysis, and predictive modeling. These insights help the company to improve its design, manufacturing, and testing processes.

---

### **Conclusion**

Boeing has successfully implemented a data lake and reporting architecture for the CAD and PLM data. The solution provides a centralized, scalable, and cost-effective platform that enables faster data access, improved data quality, and data analytics capabilities. The insights gained from the data analytics have enabled the company to improve the product development process and reduce costs. The solution can be extended to other areas of the enterprise to improve the data management and analytics capabilities.

Data Lake and Reporting Architecture for Product Development at Boeing

A prominent law firm sought a centralized repository of partner information, aggregated from multiple internal and external websites. Our team delivered a fully automated solution that:

1\. **Extracted Data** with a Python 3.9 + BeautifulSoup 4 web crawler, enriched with libraries like Requests and Selenium for dynamic content.

2\. **Transformed and Loaded** the data into a secure, scalable AWS-based infrastructure (S3, Glue, Redshift) via an automated CI/CD pipeline.

3\. **Built a Retrieval-Augmented Generation (RAG)** architecture leveraging LangChain, with vector embeddings stored in MongoDB’s VectorStore for advanced semantic querying.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FZg4U9YZxcLl2MlLFariu%2F32355887-8d36-4153-980e-bd28d606ab70.png?alt=media&token=50c303c8-f9d7-46ea-8419-618af3123b06","id":"32355887-8d36-4153-980e-bd28d606ab70","width":1583,"height":3840,"filename":"Untitled diagram-2024-12-26-124544.png","type":"image/png","caption":"","border":false}]}
```

## 

### **1.1 Fragmented Data Ecosystem**

• **Diverse Sources**: Partner profiles resided on the firm’s site, industry directories, and legal ranking websites (e.g., Chambers, LexisNexis).

• **Low Data Quality**: Reliance on manual copying/pasting and ad-hoc spreadsheets increased data inconsistency and errors.

• **Complex Inquiries**: Existing search tools were limited to keyword matching, making it difficult to answer deeper queries like “Which partner has negotiated the largest arbitration in the energy sector?”

### **1.2 Strategic Objectives**

• **Centralized Data**: Consolidate partner information into a single “source of truth.”

• **Automated Pipeline**: Eliminate manual overhead through robust data ingestion, transformation, and storage processes.

• **Intelligent Search**: Leverage modern NLP (through Large Language Models) to enable advanced retrieval and summarization.

This end-to-end system consolidated scattered partner data, unlocked complex NLP-driven queries, and significantly accelerated the firm’s research capabilities.

### **2. Technical Approach and Methodology**

### **2.1 Data Collection with Python 3.9, BeautifulSoup 4, and Selenium**

**2.1.1 Crawler Design**

• **Libraries and Frameworks**:

• **Requests** for straightforward GET/POST calls.

• **BeautifulSoup 4** to parse static HTML pages.

• **Selenium** for dynamic content extraction where JavaScript-driven elements are present (e.g., partner bios behind AJAX calls).

• **Modular Architecture**:

• **Config Files**: Each target website had a dedicated config specifying URL patterns, HTML selectors, and pagination rules.

• **Error Handling**: Implemented Python try-except blocks to handle broken links, timeouts, and captchas.

**2.1.2 JSON Conversion**

• **Schema Definition**: Leveraged Python’s dataclasses or pydantic to enforce a consistent schema (e.g., name, position, specialty, bio, etc.).

• **Data Validation**: Used built-in validators to ensure mandatory fields (e.g., email, phone number) are not null and follow standard formats.

• **Output Storage**: Exported validated data to JSON, then stored locally before upload to AWS S3.

### **2.2 ETL Pipeline with AWS and Supporting Tools**

**2.2.1 Data Ingestion into Amazon S3**

• **S3 Bucket Configuration**:

• **Versioning** enabled for audit trails.

• **Lifecycle Policies** for transitions to infrequent access and archiving.

• **Security**: Employed AWS KMS for server-side encryption (SSE-KMS), restricting keys through IAM roles.

**2.2.2 AWS Glue for Transformation**

• **Glue Crawlers**: Automated schema discovery for JSON, generating a Glue Data Catalog.

• **Glue ETL Jobs**: Written in PySpark to perform:

• **Cleansing**: Standardize date formats, unify naming conventions (e.g., “Senior Partner” vs. “Sr. Partner”).

• **Enrichment**: Cross-referenced external data (e.g., Chambers ranking) to add partner accolades.

• **Job Orchestration**: AWS Step Functions or Apache Airflow (running on Amazon MWAA) for scheduling and dependency management.

**2.2.3 Loading into Amazon Redshift**

• **Redshift Provisioning**:

• **Cluster Type**: dc2.large or ra3 nodes for scalable compute and storage.

• **Subnet Groups** in a private VPC for secure data access.

• **Data Warehouse Schema**:

• **Star Schema**: Fact tables (e.g., “Partner\_Engagements”) referencing dimensional tables (“Dim\_PartnerInfo,” “Dim\_Specialty”).

• **Metadata Tracking**: Columns for last\_updated, source\_url for auditing.

• **Performance Optimization**:

• **Sort Keys and Dist Keys** to minimize data movement for heavy queries.

• **Column Encoding** chosen by Redshift’s ANALYZE COMPRESSION function.

### **2.3 CI/CD and DevOps**

• **Version Control**: GitLab for code repositories (crawler scripts, Glue ETL jobs, and Redshift schema definitions).

• **Continuous Integration**: GitLab CI jobs running lint checks (Flake8, Black) and unit tests.

• **Docker Containerization**: Packaged the crawler code into Docker images stored on AWS ECR for reproducible runs.

• **Infrastructure as Code**: Deployed S3 buckets, Glue jobs, and Redshift clusters via AWS CloudFormation or Terraform.

### **2.4 Building an NLP-Powered Search Engine (RAG) with LangChain**

**2.4.1 Vector Embedding Generation**

• **LangChain Integration**:

• **Language Model**: Hugging Face Transformers (e.g., sentence-transformers/all-MiniLM-L6-v2) for embedding generation.

• **Embedding Pipelines**: Batch processed partner bios and specialized legal documents.

• **Data Flow**:

1\. Query Redshift for partner data.

2\. Use LangChain to transform text fields into vector embeddings.

3\. Store embeddings in MongoDB’s VectorStore.

**2.4.2 MongoDB VectorStore**

• **Schema**:

• \_id: Unique partner identifier or doc ID.

• embedding: High-dimensional float array.

• metadata: Additional fields (e.g., partner name, practice area).

• **Similarity Search**:

• **Cosine Similarity**: Implemented within the VectorStore to find nearest neighbors in embedding space.

• **Indexing**: Utilized MongoDB Atlas Search for indexing vectors and accelerating queries.

### **2.4.3 RAG Query Flow**

1\. **User Query**: Input captured via a React/Next.js web front-end or a chatbot interface built in Streamlit.

2\. **LangChain Orchestration**:

• **Retrieval**: Identifies top-k relevant partner embeddings from MongoDB.

• **Contextual Assembly**: Aggregates relevant partner details.

• **LLM Response**: Passes context to the LLM (OpenAI GPT-4 or local LLM) to generate a final, succinct answer.

3\. **Answer Delivery**: Renders a final text response along with relevant partner profiles.

By leveraging a Python –driven web crawler (BeautifulSoup, Selenium, Requests), an AWS-based ETL pipeline (S3, Glue, Redshift), and a state-of-the-art RAG approach (LangChain + MongoDB VectorStore), we delivered a future-ready platform for the firm’s legal data needs. This solution not only centralizes partner information for optimal discoverability but also empowers attorneys and researchers with intelligent, high-speed NLP queries—giving the firm a cutting-edge advantage in a competitive legal landscape.

Comprehensive System-Level State Diagram for an NLP-Driven Data Pipeline Integrating Web Crawling, AWS ETL, and Retrieval-Augmented Generation

### **Background**

As businesses grow, they often face challenges in managing their parcel shipping expenses, which can quickly become a significant cost center. In this case study, we will explore how we converted raw FedEx and UPS invoices into relation database tables and created interactive dashboards in Power BI to help our client gain visibility and control over their parcel shipping costs.

### **Objective**

**Our main objectives for this project were to:**

1. Convert raw FedEx and UPS invoices into a structured and organized database format

2. Develop interactive dashboards in Power BI to provide actionable insights into our client's parcel shipping expenses

3. Enable our client to make data-driven decisions to optimize their parcel shipping costs and improve their bottom line.

### **Solution**

To achieve our objectives, we first developed a data model by converting the raw FedEx and UPS invoices into structured database tables using SQL Server Management Studio. We identified the relevant fields in the invoices, including carrier, service type, shipment weight, shipping cost, and accessorial charges, and organized them into tables to create a relational database.

Next, we connected our database to Power BI and developed several interactive dashboards to provide visibility into our client's parcel shipping expenses. The dashboards included visualizations such as:

1. **Parcel spend by the carrier, service type, and accessorial charges**

2. **Carrier performance metrics such as on-time delivery, delivery time, and transit time**

3. **Shipping cost trends over time and by destination zones**

4. **Accessorial charge analysis to identify opportunities for cost savings**

### **Results**

By leveraging Power BI to visualize and analyze their parcel shipping expenses, our client was able to gain visibility into their shipping costs and identify areas where they could optimize their spending. The dashboards we created enabled them to make data-driven decisions about carrier selection, service types, and accessorial charges, resulting in significant cost savings

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Fb5VQ5C3e59OVWBauchED%2Fb49ad7fc-abca-49ef-86bd-f8a1e9b6f43a.png?alt=media&token=7609a62e-abca-4866-984c-c9d14ea971d6","id":"b49ad7fc-abca-49ef-86bd-f8a1e9b6f43a","width":951,"height":671,"filename":"Untitled.png","type":"image/png","caption":"FedEx and UPS analytics from Invoices","border":false}]}
```

### **Conclusion**

By converting raw FedEx and UPS invoices into a structured database and developing interactive dashboards in Power BI, we were able to help our client gain visibility and control over their parcel shipping expenses. By enabling our client to make data-driven decisions about their parcel shipping, we were able to help them optimize their spend and improve their bottom line. The use of Power BI allowed our client to identify opportunities for cost savings, and make informed decisions to improve their parcel shipping operations.

Parcel Shipping Analytics using FedEx and UPS Invoices

### **Introduction**

Firstlook AI, a leading healthcare imaging services provider, Facing challenges in visualization, schedule modification, and rule creation, Firstlook AI aimed to modernize its operations for improved efficiency and real-time insights.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FdAKp7f6jXiUZd8zmqUJD%2F6561915a-0ef1-49f0-82b2-48f5f23ba4a1.png?alt=media&token=ce0ddc92-03bd-4319-a745-b926b2de9c33","id":"6561915a-0ef1-49f0-82b2-48f5f23ba4a1","width":704,"height":496,"filename":"Untitled.png","type":"image/png","caption":"Firstlook","border":false}]}
```

### **Overview**

I conducted a comprehensive analysis of Firstlook AI's existing scheduling procedures, identifying areas for enhancement. Following collaborative workshops, the proposed solution involved harnessing big data and analytics to create a scalable and secure data platform aligned with HIPAA compliance.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FdAKp7f6jXiUZd8zmqUJD%2F696a4263-4516-43ca-9205-d283b664f4bf.png?alt=media&token=85b27074-9fd7-4501-991b-6812a4e8b5de","id":"696a4263-4516-43ca-9205-d283b664f4bf","width":1254,"height":1304,"filename":"Screenshot 2024-01-06 at 6.55.35 PM.png","type":"image/png","caption":"Solution Architecture for Firstlook","border":false}]}
```

**Azure Foundation:**

* Leveraging Azure as the foundational platform for storage, computing, and security.

* Utilizing Azure Data Factory for seamless data integration.

* Employing Azure Data Lake Storage for efficient data storage.

**Data Processing:**

* Incorporating PySpark for streamlined data processing.

* Utilizing Azure Data Warehouse for structured data storage.

**Visualization and Reporting:**

* Employing Power BI for data visualization and real-time reporting.

* Connecting Power BI to Azure Data Lake Storage through Azure Data Factory.

**Power Platform:**

* Utilizing Power Platform for building custom applications.

* Implementing Azure Logic Apps for workflow automation.

**Data Storage:**

* Storing structured data in Azure Cosmos DB for applications.

### **Results**

The implementation of the new data platform provided Firstlook AI with several benefits:

* Enhanced visibility and control over the scheduling process.

* Real-time insights into patient scheduling and capacity management.

* Increased operational efficiency and reduced errors.

* Empowered the team to make data-driven decisions, leading to overall operational improvements.

Building a Data Platform for Healthcare Scheduling

## **Enhancing Business Intelligence with Real-Time Dashboards**

### **Challenge:**

The organization faced a multifaceted challenge rooted in data fragmentation and the inability to derive real-time insights. The disparate data sources inhibited the seamless flow of information, hindering the organization's agility in decision-making. The imperative was to consolidate, analyze, and disseminate data across sales, network operations, and marketing in real time.

### **Solution:**

Leveraging an advanced technology stack, the organization strategically implemented Tableau, Snowflake, Workday, and Tableau Online to construct a cohesive and responsive analytics ecosystem. The technical architecture ensured efficient data integration, processing, and visualization, addressing the core challenges faced by the organization.

### **Technical Implementation:**

**1. Tableau and Snowflake Integration:**

* Utilized Tableau's robust visualization capabilities.

* Integrated Snowflake as the data warehouse for centralized and scalable data storage.

* Achieved seamless data flow and real-time updates through Tableau connectors.

**2. Workday Integration:**

* Incorporated Workday to streamline the integration of HR and financial data into the analytics pipeline.

* Ensured data consistency and accuracy by establishing secure API connections.

**3. Tableau Online Deployment:**

* Deployed Tableau Online to enable remote access and collaboration.

* Implemented secure authentication protocols for data privacy and user access control.

### The technical implementation facilitated the following enhancements in the sales domain:

* **Real-time Data Access for AEs:**

  * Utilized Tableau to provide AEs with real-time sales data.

  * Implemented data connectors to various sales platforms, enabling continuous data updates.

* **Data-Driven Sales Strategy:**

  * Applied advanced analytics within Tableau for trend identification and customer behavior analysis.

  * Integrated machine learning algorithms for predictive analytics in sales forecasting.

### **Results:**

The technical interventions yielded tangible results:

* **Tableau Performance Metrics:**

  * Achieved a 20% increase in Tableau dashboard loading speed.

  * Optimized SQL queries for efficient data retrieval from Snowflake.

* **Snowflake Scalability:**

  * Demonstrated a 15% reduction in network downtime through scalable and efficient data storage and processing.

* **Tableau Online Accessibility:**

  * Realized a 25% improvement in marketing ROI through collaborative data-driven decision-making on Tableau Online.

Docusign's BI with Real Time Dashboards

## Building a Cloud-Based Data Lakehouse for Enhanced Organizational Insights

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FgeTLLTvfgFpWuahcpZvr%2Fcd98f203-73b9-4efd-8eed-155002770f1c.png?alt=media&token=d6f1358a-aed5-48d9-a335-fe12e15a7e7e","id":"cd98f203-73b9-4efd-8eed-155002770f1c","width":3226,"height":1676,"filename":"Screenshot 2023-12-14 at 22.51.59.png","type":"image/png","caption":"Process Diagram ","border":false}]}
```

Securitas sought to transform its data management strategy by establishing a centralized repository for all organizational data. The primary objectives included making data accessible to everyone, creating a well-organized data source, ensuring continuous data refresh, and converting raw data into actionable insights for improved decision-making. To achieve this, the client envisioned a robust data lake-house architecture on the cloud.

### **Approach**

I adopted a phased approach, encompassing five key stages:

**1. Data Collection:** Leveraging AWS DataSync Agent, I facilitated the collection of raw data from diverse sources.

***2. Ingestion:*** Employing Airflow, I designed a seamless data ingestion process to handle and integrate large volumes of data efficiently.

***3. Storage and Metadata Processing:*** Utilizing Hive Metastore, I established a storage infrastructure with embedded metadata processing capabilities to enhance data governance.

***4. Cataloging:*** Developed a comprehensive data cataloging tool to facilitate easy navigation and understanding of the stored data.

***5. BI/Reporting:*** Established a Business Intelligence (BI) endpoint, ensuring that end-users could effortlessly derive insights from the centralized repository.

### **Challenges**

The project encountered several challenges:

***1. Diverse Data Formats:*** Collating and processing data in its raw format from various sources requires a nuanced approach.

***2. Scalability:*** Building an infrastructure capable of handling large data volumes demands meticulous planning and execution.

***3. Integration of Technologies:*** Integrating different technologies seamlessly to construct a unified system posed a significant challenge.

***4. Balancing Accessibility and Security:*** Ensuring easy access to data for end-users while upholding stringent data security and governance standards requires a delicate balance.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Fcasestudy1%2Fcbb65bd8-b321-43d9-87df-722093431ed2.png?alt=media&token=4d922d23-4862-411f-9505-072055583f88","id":"cbb65bd8-b321-43d9-87df-722093431ed2","width":1958,"height":1464,"filename":"Screenshot 2023-12-14 at 23.01.05.png","type":"image/png","caption":"Solution Architecture","border":false}]}
```

### Deliverables

The project yielded the following deliverables:

***1. Data Collection Layer:*** Facilitated the gathering of data from diverse sources through a robust data transfer layer.

***2. Data Ingestion Layer:*** Designed and implemented an efficient data ingestion process to handle large data volumes.

***3. Storage and Metadata Processing Layer:*** Established a storage infrastructure with embedded metadata processing capabilities for improved data governance.

***4. Cataloging Layer:*** Developed a user-friendly data cataloging tool to enhance accessibility and understanding of the stored data.

***5. BI/Reporting Layer:*** Implemented a Business Intelligence (BI) endpoint to empower end-users in deriving actionable insights from the centralized repository.

### **Results**

The implemented cloud-based data lakehouse architecture successfully centralized organizational data, making it accessible to all stakeholders. Raw data was transformed into actionable insights, thereby enhancing decision-making capabilities. The project exemplified a harmonious integration of technology to address the client's data management needs, ensuring a secure and governed approach to data accessibility.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Fcasestudy1%2Fa8a8b652-e94c-4b83-952b-8840d7486b7e.png?alt=media&token=e8bd589d-faba-4d0e-a6bc-339d52000756","id":"a8a8b652-e94c-4b83-952b-8840d7486b7e","width":1874,"height":1544,"filename":"Screenshot 2023-12-14 at 23.00.13.png","type":"image/png","caption":"","border":false}]}
```

Building a Cloud-Based Data Lakehouse for Securitas

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Ffulkrum%2Feea43032-d8c7-403b-9c58-4ca5aaa0c61b.png?alt=media&token=62eba508-05c4-436f-87f6-6ac2231da595","id":"eea43032-d8c7-403b-9c58-4ca5aaa0c61b","width":702,"height":200,"filename":"Group 18 (1).png","type":"image/png","caption":"","border":false}]}
```

## Fulkrum: Building AI Teams Made Easy

Fulkrum is an innovative generative AI product designed to empower users to build and manage teams of AI agents effortlessly, without requiring any technical skills. With Fulkrum's intuitive drag-and-drop interface, creating and deploying AI solutions is both simple and efficient.

### Key Features:

**- User-Friendly Interface:** Fulkrum's drag-and-drop user interface allows users to create AI agents quickly and easily, making it accessible to individuals without technical expertise.

\- **Flexible Deployment:** Fulkrum can be securely deployed on both on-premises servers and cloud servers, offering flexibility to meet various security and infrastructure needs. It includes robust firewall protections for enhanced security.

**- Data Privacy**: Fulkrum operates without training on your data, ensuring that all information is kept in a siloed manner. This commitment to data privacy means your sensitive information remains secure and isolated.

**- Model Compatibility:** Fulkrum supports any Language model (refer below illustration), offering unlimited possibilities for customization and optimization. There are no restrictions on the models you can use, allowing for a tailored AI solution that fits your specific requirements.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Ffulkrum%2F2f363272-0e09-4cb1-897f-7708b65eb672.png?alt=media&token=33f021f2-91dd-4a33-bb49-f71907d07d5c","id":"2f363272-0e09-4cb1-897f-7708b65eb672","width":1584,"height":396,"filename":"LinkedIn cover - 1.png","type":"image/png","caption":"","border":false}]}
```

What is Fulkrum

### **Introduction**

eBev, an online brewery, was facing challenges in managing its operations and sales effectively. They had a vast amount of data but lacked the necessary tools and expertise to turn it into actionable insights. I was brought on board to help eBev leverage data analytics and warehousing to optimize their operations and sales.

### **Overview**

* The client wanted to create a centralized repository for all organizational data.

* The goal was to make the data accessible to everyone and create an organized data source.

* The client wanted continuous data to refresh the repository.

* The main objective was to convert raw data into actionable insights for better decision-making.

* The client wanted to build a data lake-house architecture on the cloud to achieve this.

### **Requirements**

1. Ingest data from different sources.

2. Store data securely in Azure Data Lake Storage.

3. Perform data transformations using Azure Databricks.

4. Utilize Apache Airflow for orchestrating and scheduling the entire workflow.

5. Ensure scalability and fault tolerance.

### **Architecture**

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2FlaCzIRpsVMUQTo15jb8l%2F458189a3-426d-496c-aacc-b2f3e0d8d04d.png?alt=media&token=a931b42f-ee19-4d77-9160-ecc3c3de8790","id":"458189a3-426d-496c-aacc-b2f3e0d8d04d","width":1242,"height":692,"filename":"Screenshot 2024-01-06 at 6.39.14 PM.png","type":"image/png","caption":"eBev's Architecture diagram","border":false}]}
```

### **Description**

* **Data Sources:** Multiple data sources feed into the system, and data is ingested into Azure Data Lake Storage (ADLS).

* **Data Processing:** Azure Databricks is used for batch processing and data transformations.

* **Workflow Orchestration:** Apache Airflow orchestrates the entire workflow, scheduling tasks and triggering data processing jobs.

* **Data Analysis:** Processed data in ADLS can be queried and analyzed using Power BI for advanced analytics.

Data Analytics and Warehousing for eBev's Online Brewery

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Fsteve-jobs-%2F71b7acf1-21da-4d4f-91d2-b6be87f1a296.jpg?alt=media&token=b6fd2bff-e181-40e1-817c-acbcb7a80cc6","id":"71b7acf1-21da-4d4f-91d2-b6be87f1a296","width":728,"height":455,"filename":"steve-jobs-face-wallpaper-preview.jpg","type":"image/jpeg","caption":"","border":false}]}
```

Just finished Tony Fadell's awesome book "Build," and wow, did it have me reminiscing about the legend - Steve Jobs! Here are 5 things that really stuck with me:

**1. Simplicity Rules:** Less is more, even for tech geniuses like Jobs. He taught me to ditch the fluff and focus on creating products that are easy to use and understand. No more user manuals the size of phone books!

**2. Obsess Over Details:** Turns out, Jobs wasn't just picky, he was a detail ninja! He showed me that the little things – that perfect click of a button, the smooth curve of a case – can make a HUGE difference. ✨

**3. Tell a Story with Your Product:** Forget boring specs, Jobs emphasized that products should have a narrative. They should inspire and solve real problems, not just list features. Think iPod shuffle – tiny tunes, big freedom! ‍♀️

**4. Believe in the Impossible:** Jobs was a master dreamer, convinced you could achieve anything if you set your mind to it. His unwavering belief in the iPod, even when everyone doubted, is a constant reminder to chase those crazy ideas.

**5. Be a Demanding Mentor:** Okay, Jobs wasn't everyone's cup of tea. But Fadell showed how his tough love pushed teams to excel, to go beyond "good enough" and create true game-changers.

These are just a few nuggets from "Build" that left me inspired. If you're building anything (not just tech!), check it out

**#stevejobs** **#leadership** **#productdevelopment** **#build** **#tonyfadell** **#entrepreneurlife** **#nevergiveup**

5 Big Life Lessons I Learned About Steve Jobs (Thanks, Tony Fadell!)

## **Growth Analytics for All33 and Nuts.com**

### **Background:**

In pursuit of maximizing their digital presence and customer engagement, All33 and [Nuts.com](http://nuts.com/), two distinguished e-commerce brands, collaborated with our analytics consultancy firm. The overarching objective was to leverage analytics tools and techniques for in-depth insights into user behavior, enhanced marketing strategies, and improved customer retention.

**Scope of Work:** Our team embarked on a comprehensive analysis using Google Analytics 4 (GA4) and Mixpanel to conduct Cohort analytics, Retention analysis, Churn analysis, and Marketing Mix Modeling (MMM). The aim was to provide both All33 and [Nuts.com](http://nuts.com/) with a nuanced understanding of their digital landscapes.

### **Technical Implementation:**

1. **Cohort Analytics:**

   * Identified key user cohorts for All33 and [Nuts.com](http://nuts.com/) based on acquisition channels, devices, and geographical locations.

   * Analyzed user engagement and conversion metrics for each cohort to discern behavioral patterns over time.

2. **Retention Analysis:**

   * Leveraged GA4's advanced retention features to track user engagement over specific time intervals.

   * Identified high-performing user cohorts with sustained engagement for both All33 and [Nuts.com](http://nuts.com/).

   * Developed targeted strategies to enhance retention rates for cohorts showing underperformance.

3. **Churn Analysis:**

   * Executed churn analysis to pinpoint users who discontinued engagement with the platforms.

   * Segmented churned users based on various attributes, such as purchase history, interaction frequency, and demographics.

   * Delivered actionable insights to mitigate churn and re-engage lapsed users effectively.

4. **Marketing Mix Modeling (MMM):**

   * Integrated GA4 and Mixpanel data seamlessly to conduct a robust MMM analysis.

   * Examined the impact of various marketing channels, campaigns, and promotions on user acquisition and conversion.

   * Provided strategic recommendations to optimize marketing spend based on the most impactful channels.

```cv
gallery
{"layout":"hscroll","images":[{"src":"https://firebasestorage.googleapis.com/v0/b/maitake-project.appspot.com/o/pages%2FmCk4fivRH6XxNnQD3iA0I1Mcv9L2%2Frxf3MSYjojX2xxGMBdjk%2F409175bc-b2b1-4d9e-972e-584b5cdd612d.png?alt=media&token=389c8948-b7f6-458a-9164-2a824252b71a","id":"409175bc-b2b1-4d9e-972e-584b5cdd612d","width":758,"height":1426,"filename":"Untitled.png","type":"image/png","caption":"Process Diagram","border":false}]}
```

### **Discoveries and Anomalies:**

In the course of our analysis, a notable anomaly emerged in the user journey for [Nuts.com](http://nuts.com/). Despite successful marketing campaigns leading to heightened user acquisition, a discernible drop in conversion rates during a specific period caught our attention. Upon investigation, a technical glitch on the checkout page was identified, resulting in a higher-than-usual number of abandoned carts. Promptly rectifying this anomaly led to a substantial improvement in [Nuts.com](http://nuts.com/)'s conversion rates.

### **In a Nutshell**

**Outcome:** By implementing advanced analytics techniques and rectifying identified anomalies, our consultancy significantly improved the digital performance for both All33 and [Nuts.com](http://nuts.com/). The insights gained translated into heightened user retention, reduced churn, and optimized marketing strategies, culminating in substantial growth for both brands.

Elevating Digital Performance for All33 and Nuts.com

Do You Really Understand PMF and How to Quantify It?

Check my latest article on PMF https://abhijit.work/quantifying-pmf
mail me at abh8017@gmail.com