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Duration 21 hours
Course Outline
Foundations of Containerization in MLOps
- Assessing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for creating consistent environments
Constructing 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
- Replicating evaluation environments
- Streamlining validation processes
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Tuning runtime containers for production use
- Building scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Handling environment isolation and configuration
- Integrating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Control
- Monitoring models, images, and pipeline elements
- Managing versioned container environments
- Incorporating tools like MLflow or similar platforms
Deployment and Scaling of ML Workloads
- Executing pipelines in distributed settings
- Scaling microservices using native Docker methods
- Overseeing containerized ML systems
CI/CD for MLOps via Docker
- Automating the build and release of ML components
- Testing pipelines in containerized staging areas
- Safeguarding reproducibility and rollback capabilities
Recap and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data or model development
- A basic understanding 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.
