Machine Learning Operations (MLOps)
Machine Learning Operations (MLOps) is the set of practices that brings software engineering discipline to machine learning workflows. It covers the full lifecycle: data versioning, experiment tracking, model training, evaluation, deployment, monitoring, and retraining. The goal is to move ML models from experimental notebooks to reliable production systems that maintain their accuracy over time.
Core MLOps practices include version control for datasets and model artifacts (DVC, Weights & Biases), automated training pipelines that trigger on new data or schedule, reproducible experiment tracking (logging hyperparameters, metrics, and model checkpoints), CI/CD for model deployment (automated testing, staged rollouts), and production monitoring (tracking prediction distributions, latency, and detecting data drift that degrades accuracy).
For computer vision teams, MLOps addresses specific challenges: managing large image datasets (terabytes of images with annotations), handling GPU-intensive training jobs across cloud instances, versioning annotation changes alongside code changes, A/B testing model versions in deployment, and maintaining multiple models across different edge devices or API endpoints. Tools like MLflow, Kubeflow, and Weights & Biases are commonly used alongside platform-specific solutions.
