BSDA5014 · Knowledge Base
Machine Learning Operations (MLOps) Notes
10
Concepts
0
Facts
0
Procedures
concept
1m
218 - MLOps Lifecycle & CRISP-ML Framework
concept
1m
219 - Experiment Tracking with MLflow and W&B
concept
1m
220 - Data & Model Versioning with DVC and Git-LFS
concept
1m
221 - Feature Stores- Feast, Feature Engineering, and Online-Offline Serving
concept
1m
222 - Containers & Docker- Dockerfiles, Images, and Model Containerization
concept
1m
223 - Orchestration- Kubernetes & Kubeflow — Scaling ML Pipelines
concept
1m
224 - CI-CD for ML- GitHub Actions, Jenkins, and Continuous Training
concept
1m
225 - Model Serving- APIs, Containers, and Inference Optimization
concept
1m
226 - Model Monitoring- Data Drift, Concept Drift, and Observability
concept
1m