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Duration 21 hours
Course Outline
Containerization Foundations for MLOps
- Analyzing requirements across the ML lifecycle.
- Essential Docker concepts applicable to ML systems.
- Best practices for establishing reproducible environments.
Creating Containerized ML Training Pipelines
- Packaging model training code and its dependencies.
- Configuring training jobs utilizing Docker images.
- Handling datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Recreating consistent evaluation environments.
- Automating validation processes.
- Capturing metrics and logs from container instances.
Containerized Inference and Serving
- Architecting inference microservices.
- Optimizing runtime containers for production environments.
- Implementing scalable serving frameworks.
Orchestrating Pipelines with Docker Compose
- Coordinating complex, multi-container ML workflows.
- Managing environment isolation and configuration.
- Integrating auxiliary services, such as tracking and storage.
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline elements.
- Maintaining version-controlled container environments.
- Integrating tools like MLflow or similar platforms.
Deploying and Scaling ML Workloads
- Executing pipelines within distributed systems.
- Scaling microservices using native Docker capabilities.
- 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.
- Safeguarding reproducibility and enabling rollbacks.
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows.
- Proficiency in Python for data analysis 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.
