MLOps.
Production ML engineering — experiment tracking, model registry, CI/CD for ML, monitoring, and drift detection.
Beginner
Start here — no prior experience needed
MLOps Learning Roadmap
A structured path from experiment tracking basics to building fully automated ML platforms — covering CI/CD, monitoring, data versioning, and model serving.
MLOps Projects
Projects that build production ML infrastructure — from experiment tracking to automated retraining pipelines and monitoring systems.
Introduction to MLOps
Understand the ML lifecycle, set up experiment tracking with MLflow, and build your first reproducible ML pipeline.
Intermediate
For developers with core concepts down
MLOps Experiment Tracking
Track experiments systematically with MLflow — log parameters, metrics, artifacts, compare runs, and build a model registry.
Model Serving and Deployment
Deploy trained ML models to production — REST APIs with FastAPI, BentoML, batch inference, and model versioning strategies.
MLOps Model Monitoring
Detect data drift, monitor model performance, set up alerts, and build retraining triggers for production ML systems.
MLOps CI/CD for Machine Learning
Automate ML workflows with GitHub Actions — test data quality, validate models, and deploy only when performance thresholds are met.
MLOps Data Versioning with DVC
Version datasets and models alongside code using DVC — reproducible pipelines, remote storage, and experiment tracking.