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Course Outline
Containerization Fundamentals for MLOps
- Reviewing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for ensuring reproducible 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 Evaluation
- Replicating evaluation environments
- Automating validation processes
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Optimizing runtime containers for production settings
- Establishing scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Coordinating multi-container ML workflows
- Managing environment isolation and configuration
- Integrating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline elements
- Maintaining version-controlled container environments
- Integrating with MLflow or similar platforms
Deploying and Scaling ML Workloads
- Executing pipelines in distributed setups
- Scaling microservices through Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerized staging areas
- Guaranteeing reproducibility and facilitating rollbacks
Conclusion and Future Steps
Requirements
- A solid grasp of machine learning workflows
- Practical experience with Python for data or model development
- Basic knowledge of container concepts
Target Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
21 Hours
Testimonials (3)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin
