MLOps and Model Deployment Tutorial
Build reproducible, governed, observable machine-learning delivery systems from data validation through deployment, monitoring, and retraining.
Start learning MLOps and Model Deployment →
Course Contents
MLOps Foundations
- MLOps Lifecycle and System Boundaries
- From Notebook Experiment to Production Product
- Roles, Environments, and MLOps Maturity
- Design a Reproducible Project Structure
Data and Feature Pipelines
- Data Contracts and Schema Validation
- Dataset Versioning and Lineage
- Feature Engineering Pipelines and Feature Stores
- Prevent Training-Serving Skew and Data Leakage
Experiments and Artifacts
- Experiment Tracking with MLflow
- Parameters, Metrics, Artifacts, and Tags
- Reproducible Environments and Dependency Locking
- Model Packaging, Signatures, and the Model Registry
Pipeline Automation
- Orchestrate Training Pipelines
- Pipeline Components, Caching, and Idempotency
- CI for Data, Features, and Model Code
- Evaluation Gates and Continuous Training
Serving Models
- Batch, Online, Streaming, and Edge Inference
- Build a Typed Inference API with FastAPI
- Containerize Models and Manage Dependencies
- Deploy Model Services on Kubernetes
Safe Model Delivery
- Model Versioning and Environment Promotion
- Shadow, Canary, and Blue-Green Deployments
- A-B Tests and Online Experimentation
- Fallbacks, Rollbacks, and Graceful Degradation
Monitoring and Governance
- Service Metrics, Logs, Traces, and SLOs
- Data Drift, Concept Drift, and Performance Decay
- Bias, Explainability, Privacy, and Model Risk
- Alerts, Incident Response, and Retraining Policies
