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Duration 21 hours
Course Outline
Foundations of MLOps Containerization
- Analyzing ML lifecycle requirements
- Core Docker concepts applicable to ML systems
- Best practices for ensuring environment reproducibility
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
- Recreating consistent evaluation environments
- Streamlining validation workflows
- Extracting metrics and logs from containerized processes
Containerized Inference and Serving
- Architecting inference microservices
- Tuning runtime containers for production readiness
- Building scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Coordinating multi-container ML operations
- Managing environment isolation and configuration
- Integrating auxiliary services such as tracking and storage
ML Model Versioning and Lifecycle Control
- Monitoring models, images, and pipeline elements
- Maintaining version-controlled container setups
- Incorporating tools like MLflow or equivalents
Deploying and Scaling ML Workloads
- Executing pipelines across distributed systems
- Scaling microservices using native Docker capabilities
- Monitoring performance in containerized ML ecosystems
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML assets
- Validating pipelines in containerized staging contexts
- Safeguarding reproducibility and enabling rollbacks
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows.
- Proficiency in Python for data or model development.
- Basic knowledge of containerization 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.
