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
Testimonials (2)
Encouraging and openness to expanding the discussion on topics related to the training scope but with the specific context of our company
Michal Koscinski - Volkswagen Poznan Sp. z o.o.
Course - Docker, Kubernetes and OpenShift 3 for Administrators
Machine Translated
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
