Course Outline
Introduction to Containerization in AI & ML
- Fundamental principles of containerization
- The suitability of containers for ML workloads
- Distinguishing features between containers and virtual machines
Managing Docker Images and Containers
- Comprehending images, layers, and image registries
- Oversight of containers for ML experimentation
- Efficient utilization of the Docker CLI
Packaging ML Environments
- Readying ML codebases for container deployment
- Administration of Python environments and dependencies
- Incorporation of CUDA and GPU support
Creating Dockerfiles for Machine Learning
- Designing effective Dockerfiles for ML projects
- Best practices for ensuring performance and maintainability
- Implementation of multi-stage builds
Encapsulating ML Models and Pipelines
- Containerization of trained models
- Strategies for data management and storage
- Deployment of reproducible end-to-end workflows
Operationalizing Containerized ML Services
- Configuration of API endpoints for model inference
- Service scaling using Docker Compose
- Monitoring of runtime behavior
Security and Compliance Perspectives
- Establishment of secure container configurations
- Control of access rights and credentials
- Protection of sensitive ML assets
Production Deployment Strategies
- Publication of images to container registries
- Deployment within on-premises or cloud infrastructure
- Version management and updates for production services
Conclusion and Path Forward
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python or comparable programming languages
- Knowledge of fundamental Linux command-line operations
Target Audience
- ML engineers responsible for deploying models to production
- Data scientists focused on maintaining reproducible experiment environments
- AI developers creating scalable, container-based applications
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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.
