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Duration 21 hours
Course Outline
Core Concepts of Containerization for MLOps
- Analyzing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for establishing reproducible environments
Creating Containerized ML Training Pipelines
- Encapsulating model training code and dependencies
- Setting up training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Assessment
- Recreating consistent evaluation environments
- Streamlining validation workflows through automation
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Refining runtime containers for production readiness
- Developing scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Handling environment isolation and configuration
- Incorporating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Oversight
- Monitoring models, images, and pipeline elements
- Maintaining version-controlled container environments
- Integrating tools such as MLflow or equivalents
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings
- Scaling microservices through Docker-native 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
- Guaranteeing reproducibility and rollback capabilities
Overview and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Practical experience with Python for data or model development
- Basic familiarity with container fundamentals
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.
