MLOps: Machine Learning Operations
Monitor model health in production with live accuracy, drift, latency, and throughput metrics. Watch the automated CI/CD pipeline retrain and redeploy models when drift is detected, configure auto-retrain thresholds, and run A/B tests between model versions.
Learn MLOps: Machine Learning Operations on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
MLOps Concepts
Learn the core concepts of MLOps: the full ML lifecycle, model versioning, CI/CD for ML, and taking models from notebooks to production.
Developing Machine Learning Models for Production
Learn how to train, document, maintain, and scale ML models. Covers model documentation, technical debt, and production-grade code.
MLOps Deployment and Life Cycling
Master the modern MLOps framework, model deployment strategies, drift detection, monitoring, and minimizing technical debt.
Fully Automated MLOps
Implement CI/CD/CT/CM pipelines for ML. Automate retraining, model testing, and deployment with advanced MLOps architecture patterns.
Introduction to MLflow
Track experiments, package models, and manage the ML lifecycle with MLflow — the industry-standard open-source MLOps platform.
MLOps Fundamentals
The complete MLOps skill track. Go from local notebooks to fully automated production pipelines with monitoring and CI/CD.
