Machine Learning in production
MLOps, model versioning and monitoring: what separates an experiment from a system that creates value.
Deploying Machine Learning models to production requires a systematic approach that goes beyond training. MLOps practices are needed to guarantee quality, reproducibility and continuous monitoring of models.
Good practices include model and data versioning (MLflow, DVC), ML-specific CI/CD pipelines, data and model drift monitoring, and A/B testing to validate incremental improvements.
The architecture of production ML systems must account for autoscaling, inference latency, compute cost and data governance. With large language models, systematic evaluation of answers and safety controls join the list.
Breitner Enrique González AngaritaHead of Architecture & Development · IFX Networks
