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Duration 21 hours
Course Outline
Foundations of Containerization in MLOps
- Examining ML lifecycle requirements.
- Essential Docker concepts for ML systems.
- Best practices for creating reproducible environments.
Constructing Containerized ML Training Pipelines
- Packaging model training code and dependencies.
- Setting up training jobs with Docker images.
- Handling datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Recreating evaluation environments consistently.
- Streamlining validation workflows through automation.
- Recording metrics and logs from containerized processes.
Containerized Inference and Serving
- Architecting inference microservices.
- Tuning runtime containers for production use.
- Deploying scalable serving architectures.
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows.
- Handling environment isolation and configuration.
- Integrating auxiliary services such as tracking and storage.
ML Model Versioning and Lifecycle Oversight
- Monitoring models, images, and pipeline elements.
- Maintaining version-controlled container environments.
- Integrating tools like MLflow or equivalents.
Scaling and Deploying ML Workloads
- Executing pipelines in distributed setups.
- Scaling microservices via Docker-native methods.
- Supervising containerized ML systems.
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components.
- Testing pipelines in containerized staging areas.
- Safeguarding reproducibility and enabling rollbacks.
Conclusion and Further Steps
Requirements
- A solid 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.
