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Machine Learning Operations (MLOps) is an emerging discipline that aims to integrate the practices of machine learning and operations to streamline the end-to-end process of developing, deploying, and managing ML models. The role of the MLOps engineer is crucial in bridging the gap between machine learning (ML) development and production deployment to have smooth and efficient deployment, scaling, and maintenance of machine learning (ML) models. In this post, I want to give an overview of the key responsibilities and activities associated with the role: ...

The rise of AI-powered coding assistants like GitHub Copilot, Cursor, and various autonomous agents built on Claude or GPT-4 has fundamentally changed how we write code. These tools can read your codebase, suggest changes, and even execute commands. This capability is powerful, but it also raises a big security concern: you’re giving an AI system access to your development environment. The question isn’t whether these tools are malicious; it’s about implementing proper isolation as a matter of principle. ...