MLOps: Bridging Development and Production
Streamline machine learning workflows from model experimentation to deployment, monitoring, and maintenance using industry-standard practices.
Streamline machine learning workflows from model experimentation to deployment, monitoring, and maintenance using industry-standard practices.
ML-ops combines DevOps practices with machine learning workflows to enable fast iteration, collaboration, and scalable deployment of models in production environments.
12 Chapters
Comprehensive ML-ops Journey
Master these foundational components of production-grade machine learning systems
Structured experimentation with versioned datasets, iterative model development, and automated hyperparameter tuning.
Containerization, API gateway management, A/B testing frameworks, and infrastructure-as-code patterns for ML services.
Performance drift analysis, input data validation, drift detection, and feedback loops for model updates.
Elevate your ML infrastructure with enterprise-ready pipelines and collaboration frameworks
Implement ML model registry systems for tracking training runs, hyperparameters, and artifact storage.
Automate model testing, validation, and promotion to staging/production environments with GitOps patterns.
Integrate notebook environments, model sharing, and collaborative annotation systems for ML teams.
Explore industry-standard platforms and libraries for MLOps implementation
Experiment tracking, model management, and deployment lifecycle tooling for ML projects