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Read: MLOps Lifecycle & CRISP-ML Framework
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Machine Learning Operations (MLOps)
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MLOps Lifecycle & CRISP-ML Framework
Deep dive into patterns and axioms.
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Experiment Tracking with MLflow and W&B
Deep dive into patterns and axioms.
noteimported
Data & Model Versioning with DVC and Git-LFS
Deep dive into patterns and axioms.
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Feature Stores: Feast, Feature Engineering, and Online/Offline Serving
Deep dive into patterns and axioms.
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Containers & Docker: Dockerfiles, Images, and Model Containerization
Deep dive into patterns and axioms.
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Orchestration: Kubernetes & Kubeflow — Scaling ML Pipelines
Deep dive into patterns and axioms.
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CI/CD for ML: GitHub Actions, Jenkins, and Continuous Training
Deep dive into patterns and axioms.
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Model Serving: APIs, Containers, and Inference Optimization
Deep dive into patterns and axioms.
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Model Monitoring: Data Drift, Concept Drift, and Observability
Deep dive into patterns and axioms.
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A/B Testing & Shadow Deployment: Canary Releases and Rollout Strategies
Deep dive into patterns and axioms.