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 the 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
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 €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
Contact us for an exact quote and to hear our latest promotions
Public Training
Please see our public courses
Testimonials (5)
He explained everything, not only k8s notions.
Stefan Voinea - EMAG IT Research S.R.L
Course - Certified Kubernetes Application Developer (CKAD) - exam preparation
Depth of knowledge of the trainer
Grant Miller - BMW
Course - Certified Kubernetes Administrator (CKA) - exam preparation
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Excellent content
Alan Kavanagh - FINEOS Corporation Ltd
Course - Docker from Basic to Advanced
Labs and technical discussions.
