.. _ml_blog: ML Blog ======= Welcome to my machine learning blog! Here you'll find in-depth articles on various ML topics, from fundamental concepts to advanced techniques. .. **Machine Learning Articles** .. ============================ .. toctree:: :maxdepth: 1 :numbered: :caption: Table of Contents climbing_the_ladder assumptions_in_machine_learning inductive_biases_in_machine_learning ecoki_system_design_public **Article Details** ~~~~~~~~~~~~~~~~~~ :doc:`climbing_the_ladder` **Published:** August 2025 | **Reading Time:** 15-20 minutes A deep dive into the four paradigms of scientific discovery, from empirical experimentation to computational simulations and data-driven AI. Explore how my journey mirrors the evolution of scientific discovery and the importance of computational irreducibility. :doc:`assumptions_in_machine_learning` **Published:** August 2025 | **Reading Time:** 15-20 minutes A comprehensive guide exploring the fundamental assumptions behind major machine learning model classes. Learn about linear models, tree-based models, time series models, and more. Understand what happens when assumptions break down and how to address violations. :doc:`inductive_biases_in_machine_learning` **Published:** August 2025 | **Reading Time:** 10-15 minutes Explore how inductive biases shape machine learning models and influence their learning capabilities. Understand the trade-offs between different architectural choices and how they affect model performance. :doc:`ecoki_system_design_public` **Published:** December 2025 | **Reading Time:** 12-15 minutes Lessons from designing a low-code ML platform for industrial IoT. Explore the five-layer architecture, key design patterns (Strategy, Composition over Inheritance), and the trade-offs involved in building production-grade systems that balance power with accessibility. **Featured Resource: LLM & RAG Systems** ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. note:: 🔗 `Advanced Retrieval and Re-ranking `_ A comprehensive documentation site covering the latest research and practical techniques for building RAG (Retrieval-Augmented Generation) systems. Topics include: - **Two-Stage Pipeline Architecture** — Retrieval for candidate selection, re-ranking for precision - **Building RAG Pipelines** — From MVP to production-ready systems - **Hard Negative Mining** — Advanced techniques for training dense retrievers - **Cross-Encoders & LLM Re-rankers** — State-of-the-art re-ranking methods - **30+ Library Comparisons** — LangChain, LlamaIndex, ColBERT, and more - **Benchmarks & Datasets** — BEIR, MS MARCO, Natural Questions **Upcoming Posts** ~~~~~~~~~~~~~~~~~~ * **Reinforcement Learning Fundamentals** - From Q-learning to policy gradients * **Production ML Systems** - Deployment, monitoring, and maintenance strategies