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Course Outline
Foundations of Containerization for MLOps
- Understanding ML lifecycle requirements
- Key Docker concepts for ML systems
- Best practices for reproducible environments
Building Containerized ML Training Pipelines
- Packaging model training code and dependencies
- Configuring training jobs using Docker images
- Managing datasets and artifacts in containers
Containerizing Validation and Model Evaluation
- Reproducing evaluation environments
- Automating validation workflows
- Capturing metrics and logs from containers
Containerized Inference and Serving
- Designing inference microservices
- Optimizing runtime containers for production
- Implementing scalable serving architectures
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows
- Environment isolation and configuration management
- Integrating supporting services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Version-controlled container environments
- Integrating MLflow or similar tools
Deploying and Scaling ML Workloads
- Running pipelines in distributed environments
- Scaling microservices using Docker-native approaches
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating builds and deployment of ML components
- Testing pipelines in containerized staging environments
- Ensuring reproducibility and rollbacks
Summary and Next Steps
Requirements
- An understanding of machine learning workflows
- Experience with Python for data or model development
- Familiarity with the fundamentals of containers
Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
21 Hours
Testimonials (1)
The trainer's broad knowledge, his abilities to solve issues that spontaneously occurred during the practice sessions. Also, the exercises themselves are adequate to help fix the subjects contained in the course.
