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Course Outline
Foundations of Containerization for MLOps
- Understanding the lifecycle requirements of ML
- Key Docker concepts applicable to ML systems
- Best practices for maintaining reproducible environments
Building Containerized ML Training Pipelines
- Packaging model training code along with its dependencies
- Configuring training jobs via Docker images
- Managing datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Replicating evaluation environments accurately
- Automating validation workflows
- Capturing metrics and logs from containerized processes
Containerized Inference and Serving
- Designing inference microservices
- Optimizing runtime containers for production use
- Implementing scalable serving architectures
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows
- Managing environment isolation and configuration
- Integrating supporting services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Maintaining version-controlled container environments
- Integrating MLflow or comparable tools
Deploying and Scaling ML Workloads
- Executing pipelines in distributed environments
- Scaling microservices using Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines within containerized staging environments
- Ensuring reproducibility and enabling rollbacks
Summary and Next Steps
Requirements
- A foundational understanding of machine learning workflows
- Practical experience with Python for data processing or model development
- Familiarity with core container concepts
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.
