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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Create End-to-End MLOps Pipelines
- Build pipelines for evaluation, deployment, human-in-the-loop, and governance before full rollout.
- Enforce traceability and reproducibility: record data, code, and environment for each model version.
Agents Add Significant Complexity
- Agents are harder to control than standard ML models because they involve many moving parts.
- Maria Vechtomova recommends specialized, narrow agents or workflows rather than generic ones.
Treat LLM Outputs Like Model Predictions
- For API-based features, treat LLM outputs like model predictions and label them for evaluation.
- Use metrics like accuracy and false-positive/negative costs to steer trade-offs toward business value.