Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Containerization for MLOps
- Analyzing the requirements of 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 via Docker images
- Managing datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Recreating consistent evaluation environments
- Automating validation workflows for efficiency
- Capturing and analyzing metrics and logs from containers
Containerized Inference and Serving
- Designing efficient inference microservices
- Optimizing runtime containers for production use
- Implementing scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Coordinating complex, multi-container ML workflows
- Managing environment isolation and configuration
- Integrating supporting services such as tracking and storage
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Implementing version-controlled container environments
- Integrating tools like MLflow or equivalent solutions
Deploying and Scaling ML Workloads
- Executing pipelines in distributed environments
- Scaling microservices using Docker-native methods
- Monitoring the health of containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment processes for ML components
- Testing pipelines within containerized staging environments
- Ensuring reproducibility and effective rollback capabilities
Summary and Future Steps
Requirements
- A solid understanding of machine learning workflows
- Practical experience with Python for data analysis or model development
- Familiarity with the fundamental concepts of containers
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
