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Intermediate
Model Deployment
Learn to deploy ML models to production with APIs, containerization, and MLOps best practices. Build scalable, reliable ML systems.
15-20 hours total12 modulesCertificate included
Course Modules
1
Introduction to MLOps
The ML lifecycle in production
25 min
2
Model Serialization
Pickle, ONNX, and SavedModel
35 min
3
Building REST APIs
Flask and FastAPI for ML
50 min
4
Request/Response Design
API contracts and validation
40 min
5
Docker Fundamentals
Containerizing ML applications
55 min
6
Docker Compose
Multi-container ML systems
40 min
7
Kubernetes Basics
Orchestrating ML workloads
60 min
8
Model Serving Platforms
TensorFlow Serving, TorchServe
50 min
9
Cloud Deployment
AWS, GCP, and Azure ML
65 min
10
Model Monitoring
Tracking performance in production
55 min
11
CI/CD for ML
Automated testing and deployment
50 min
12
Capstone: Deploy an ML API
End-to-end deployment project
120 min