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Duration 21 hours
Course Outline
Foundations of Containerization in MLOps
- Reviewing requirements across the ML lifecycle
- Essential Docker concepts for ML systems
- Best practices for ensuring environment reproducibility
Constructing Containerized ML Training Pipelines
- Packaging model training code and its dependencies
- Setting up training jobs with Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Assessment
- Recreating consistent evaluation environments
- Automating the validation process
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Optimizing runtime containers for production use
- Implementing scalable serving designs
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Isolating environments and managing configuration
- Integrating auxiliary services (such as tracking and storage)
Managing ML Model Versioning and Lifecycle
- Tracking models, images, and pipeline elements
- Utilizing version-controlled container setups
- Integrating tools like MLflow or similar solutions
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings
- Scaling microservices using native Docker methods
- Monitoring containerized ML systems
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerized staging environments
- Safeguarding reproducibility and rollback capabilities
Wrap-up and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data processing or model development
- Basic knowledge of container concepts
Target Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
multi-tiered, structured course programme.
