MLOps — Machine Learning in Production
MLOps brings engineering discipline to machine learning: versioning data and models, automating training and deployment pipelines, monitoring for drift and regressions, and gating releases with evaluation. These guides focus on operating ML and LLM systems reliably and cost-effectively at scale.
You'll find practical guidance on inference cost, observability for AI systems, deployment strategies, and the operational reviews that keep quality and spend under control.
Frequently asked questions
What is MLOps and why is it important?
MLOps is the practice of deploying, monitoring, and maintaining machine learning models in production reliably. It matters because a model that works in a notebook can fail in production from data drift, latency, or cost issues — MLOps provides the automation and monitoring to prevent that.
How is MLOps different from DevOps?
MLOps builds on DevOps but adds ML-specific concerns: versioning datasets and models (not just code), monitoring for model/data drift, and evaluating model quality — because ML systems depend on data and probabilistic outputs, not just deterministic code.
