Thoughts on Machine Learning, AI, Technology, and the Future
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.
Read moreStop treating ML experiments as disposable! Learn why elevating experiment tracking to first-class infrastructure is crucial for robust MLOps, reproducibility & Software 2.0.
Read moreA 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.
Read moreThe history of Full stack, Machine learning which results in the mergence of the ML full stack engineer
Read moreProblems in the ML ecosystem. Fragmentation in machine learning, that keeps preventing the stack from growing higher. How I took a stab at the issue.
Read moreA guide to setting up a productive Python environment using Pipenv and Pyenv
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