Notes on AI, ML, data & the engineering behind them.

Weekly revisions — the 6-month plan· 26 chapters

One revision per week walking through the previous week’s five sessions with recall prompts.

Microsoft Web Stack — novice to fluent· 22 chapters

22-chapter self-study plan from developer tooling through resilience, security, and shipping.

How Transformers actually attend

Beyond the textbook diagrams — what a single attention head is really computing, how multi-head splits the world, and why scaling laws keep rewarding bigger context.

Designing a recommendation system from scratch

Retrieval vs ranking, candidate generation, freshness vs relevance — the tradeoffs every real recommender lives by.

Kafka 101 for ML engineers

Topics, partitions, consumer groups — the parts of Kafka that actually matter when you put ML features behind it.

Overall Engineering Clarity — Data, Distributed Systems and AI (Deep Dive)

A long-form, primary study companion. Internals, flows, decision trees, code, and Q&A with reasoning across Spark, lakehouse, graphs, search, LLMs, RAG/agents, distributed HLD, governance, modeling, SQL, JVM, Python, K8s and CI/CD.

Data Infrastructure for AI & Experimentation at Scale

A comprehensive deep-dive into the data backbone powering ML, personalization, experimentation, and GenAI on modern streaming platforms

Automating audio generation

From text , generate audio files and publishing them to webapp

Deploying scripts as an API in Azure

Deploying local python scripts and converting them as an API.

Learning how to build an recommendation system from initial signals

From a few initial adopters of a product, how we can target new set of users who are more likely can use the product

Building windows app and publishing to app store.

Exploring the fundamentals of building an .exe file from scratch, including C++ compilation, object files, linking, DLLs, and more.

Stanford CS229: Machine Learning Course

CS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical ); and reinforcement learning among other topics

Best LLM Prompt for understanding any concept in-depth

Use this prompt for gathering information all at one place

Introduction to TensorFlow on Google Cloud

Diving deep into a Google Skill boost

Diving deep into Tiktok recommendation engine

Going deeper into the video rec repo Monolith and paper produced by Bytedance

Basics of Azure AI Foundry

A self-sufficient guide to Azure AI Foundry — what it is, how the hub/project/deployment model works, how to ship a grounded agent end-to-end with SDK + Bicep, and the security/eval/cost levers you cannot skip.

Reading on MEDICAL ERROR DETECTION AND CORRECTION IN CLINICAL NOTES

This blog explores a paper on detecting and correcting medical errors in clinical notes using Large Language Models (LLMs)

Apache Airflow - open source orchestration engine

Architecture of Apache Airflow, how DAGs help design complex flows and dependencies, and how we can leverage Apache airflow to train a ML Model and monitor.

Taking the Azure Fabric Ignite Edition Challenges to Complete

Microsoft Learn Challenge conducting a challenge to get good in few of the challenges which are super useful to complete to gain knowledge on Microsoft Fabric.

Exploring different services in GCP

Exploration and documentation of different services offered in GCP

Starting a company in India

Documenting the process of starting a company in india

Exploring Azure Data Explorer and Best Practices

A self-sufficient deep-dive on Azure Data Explorer (ADX/Kusto) — architecture, the KQL language from zero to advanced, ingestion patterns, performance/cost levers, and operational best practices.

Google 5 Day Gen AI course with interactive hands-on practice

Google and Kaggle provided good summary course on Gen AI , the blog contains details and highlights of the course.

RAG architecture basics and workings

Retrieval-Augmented Generation from first principles — embeddings, vector databases, chunking, retrieval, prompt construction, evaluation, and common failure modes — enough depth to build one yourself.

AI Voice chatting to help with Customer support use-cases

Using current speech augmented LLMs (SpeechLLMs) with realtime voice modality to understand user issues and to provide support and solutions.

Audio to Video Generation Using Replit AI and Deploy as an Azure Webapp

Tool to convert an uploaded audio mixed with an image and generate a video format with image and uploaded audio in the video format.

Deploying Web Applications in Azure with Docker

A self-sufficient, production-minded walkthrough — from Docker internals to a hardened deploy on Azure App Service / Container Apps.

Orchestrating ML Pipelines with Azure Data Factory

Leveraging Azure Data Factory for Scalable and Efficient Machine Learning Workflows

Developing a Astrology webapp version 1.

Initial version 1 of Astro app hosted at astroyuga.com

Improving the UI of this blogging app - V2

Changing the UI layout and improving the experience by modernizing the UI with custom styling

Leveraging CURSOR and Azure Services for Rapid Web Deployment

Accelerating Development and Deployment Cycles with AI Tools