Technical insights on machine learning, MLOps, AI engineering, and software development. Sharing practical experiences and deep dives into the latest trends in tech.
How early architectural decisions create a flywheel effect that accelerates rather than hinders your path to production. Discover the Nimble Flywheel framework for scaling ML from prototype to production.
From dependency hell to device confusion - I built a modality-aware cookiecutter template that gets you from idea to training in minutes, not hours. Here's why modern ML projects need modern tooling.
A month-long experiment with Claude Code that replaced Instagram reels with AI-powered coding sessions. How I shipped more projects than ever before while paradoxically becoming worse at programming from scratch.
Master efficient ML development with the Pre-Flight, In-Flight, Post-Flight framework. Learn how to catch bugs before expensive training runs, monitor training in real-time, and evaluate models beyond vanity metrics. Stop wasting millions on failed ML experiments.
A wild 2 AM thought experiment: What if AI agents could orchestrate your entire MLOps stack through MCP? From data drift to deployment, explore the future of autonomous ML operations.
Master the Agent2Agent (A2A) Protocol! Learn how Google's open standard enables secure AI agent communication, discover vs MCP differences, explore authentication, monetization, and orchestration. Complete Python examples included.
Explore six pivotal technological shifts - Autonomous Economy, Radical Abstraction, Modularity & Composability, Ecosystem Dynamics, Ambient Intelligence, and Next-Gen UX - that are converging to reshape our digital future. A strategic framework for product builders.
Dive into the AI agent framework wars! Compare OpenAI, Google ADK, AWS Bedrock, Smol Agents. Explore multi-agent systems & future trends. Expert analysis.
Stop treating ML experiments as disposable! Learn why elevating experiment tracking to first-class infrastructure is crucial for robust MLOps, reproducibility & Software 2.0.
A comprehensive overview of modern ML training infrastructure, covering cloud agnosticism, spot instances, on-premise solutions, heterogeneous hardware, distributed training, and emerging GPU cloud providers.
A practical guide for startup founders on when and how to invest in MLOps - from early stage flexibility to scaling infrastructure, with key principles and pitfalls to avoid.
Explore why building tech for India means rethinking everything from the ground up—addressing local habits, scale, price sensitivity, and cultural diversity with first-principles design.
The history of Full stack, Machine learning which results in the mergence of the ML full stack engineer
Problems in the ML ecosystem. Fragmentation in machine learning, that keeps preventing the stack from growing higher. How I took a stab at the issue.
A guide to setting up a productive Python environment using Pipenv and Pyenv