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Duration 21 hours
Course Outline
Foundations of Containerization for MLOps
- Identifying ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for creating reproducible environments
Creating Containerized ML Training Pipelines
- Encapsulating model training code and dependencies
- Setting up training jobs using Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Replicating evaluation environments
- Streamlining validation workflows
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Refining runtime containers for production
- Implementing scalable serving architectures
Pipeline Orchestration using Docker Compose
- Managing multi-container ML workflows
- Isolating environments and managing configuration
- Integrating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Managing version-controlled container environments
- Integrating MLflow or similar tools
Deploying and Scaling ML Workloads
- Executing pipelines in distributed environments
- Scaling microservices with Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps using Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerized staging environments
- Ensuring reproducibility and rollback capabilities
Summary and Next Steps
Requirements
- A foundational grasp of machine learning workflows
- Proficiency in Python for data or model development
- Basic knowledge of 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.
