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Duration 21 hours
Course Outline
Foundations of Containerization in MLOps
- Analyzing the specific requirements of the ML lifecycle.
- Core Docker concepts relevant to ML system architecture.
- Best practices for establishing reproducible development environments.
Constructing Containerized ML Training Pipelines
- Encapsulating model training code and its associated dependencies.
- Setting up training jobs utilizing Docker images.
- Handling datasets and artifacts within containerized frameworks.
Containerizing Validation and Model Assessment
- Recreating consistent evaluation environments.
- Streamlining validation processes through automation.
- Collecting metrics and logs directly from containers.
Containerized Inference and Serving
- Architecting inference microservices.
- Tuning runtime containers for production performance.
- Implementing high-scalability serving patterns.
Orchestrating Pipelines via Docker Compose
- Managing complex, multi-container ML workflows.
- Handling environment isolation and configuration control.
- 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.
- Incorporating tools like MLflow for enhanced management.
Deploying and Scaling ML Workloads
- Executing pipelines across distributed systems.
- Scaling microservices using Docker-native methodologies.
- Monitoring the health of containerized ML systems.
CI/CD Integration for MLOps with Docker
- Automating the build and release cycles for ML components.
- Validating pipelines within containerized staging environments.
- Safeguarding reproducibility and facilitating rollbacks.
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows.
- Hands-on experience with Python for data analysis or model development.
- A working 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.
