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Course Outline
Introduction to Containerization for AI & ML
- Core concepts of containerization
- Why containers are ideal for ML workloads
- Key differences between containers and virtual machines
Working with Docker Images and Containers
- Understanding images, layers, and registries
- Managing containers for ML experimentation
- Using Docker CLI efficiently
Packaging ML Environments
- Preparing ML codebases for containerization
- Managing Python environments and dependencies
- Integrating CUDA and GPU support
Building Dockerfiles for Machine Learning
- Structuring Dockerfiles for ML projects
- Best practices for performance and maintainability
- Using multi-stage builds
Containerizing ML Models and Pipelines
- Packaging trained models into containers
- Managing data and storage strategies
- Deploying reproducible end-to-end workflows
Running Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services with Docker Compose
- Monitoring runtime behavior
Security and Compliance Considerations
- Ensuring secure container configurations
- Managing access and credentials
- Handling confidential ML assets
Deploying to Production Environments
- Publishing images to container registries
- Deploying containers in on-prem or cloud setups
- Versioning and updating production services
Summary and Next Steps
Requirements
- An understanding of machine learning workflows
- Experience with Python or similar programming languages
- Familiarity with basic Linux command-line operations
Audience
- ML engineers deploying models to production
- Data scientists managing reproducible experiment environments
- AI developers building scalable containerized applications
14 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £3800 online delivery, based on a group of 2 delegates, £1200 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (5)
OC is new to us and we learnt alot and the labs were excellent
sharkey dollie
Course - OpenShift 4 for Administrators
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Labs and technical discussions.
Dinesh Panchal - AXA XL
Course - Advanced Docker
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I mostly enjoyed the knowledge of the trainer.
