Beyond Coding The AI Skills Software Engineers Need to Learn Now
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Jan 7, 2026 Maria Vechtomova, an MLOps expert and co-founder of Marvelous MLOps and Couchy, shares invaluable insights on the complexities of deploying AI systems. She emphasizes the critical transition from a proof of concept to production, detailing essential MLOps principles and evaluation strategies. Maria highlights the security risks associated with autonomous agents and the necessity for rigorous monitoring. Additionally, she offers practical productivity tips for leveraging AI tools effectively, helping software engineers navigate the evolving landscape of AI.
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AI Hype Brought Monitoring Into Spotlight
- The LLM hype pushed long-overlooked areas like monitoring and serving into focus.
- Maria Vechtomova says software engineers now care about production problems previously ignored by ML teams.
Learn Data Science Basics First
- Learn core data science basics because evaluation and data gathering matter for production AI.
- Define expected outputs and gather labeled examples before claiming your system works.
Trace Agent Steps And Alert On Anomalies
- Log detailed traces of agent steps (LLM calls, tool calls, reasoning) and use them for monitoring.
- Alert on anomalies like sudden changes in tool-calling frequency to detect failures early.