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 Duration 14 hours

Course Outline

Foundations of Containerization for AI & ML

  • Essential principles of containerization
  • The suitability of containers for ML workloads
  • Distinguishing features between containers and virtual machines

Handling Docker Images and Containers

  • Exploring images, layers, and container registries
  • Managing containers to support ML experimentation
  • Efficient utilization of the Docker CLI

Encapsulating ML Environments

  • Preparing ML codebases for containerization
  • Overseeing Python environments and managing dependencies
  • Integrating CUDA and GPU support

Crafting Dockerfiles for Machine Learning

  • Designing Dockerfiles tailored for ML projects
  • Adopting best practices for performance and maintainability
  • Leveraging multi-stage builds

Containerizing Models and Pipelines

  • Packaging trained models into containerized formats
  • Strategizing data management and storage solutions
  • Deploying reproducible end-to-end workflows

Operationalizing Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services effectively with Docker Compose
  • Monitoring runtime performance and behavior

Security and Compliance Frameworks

  • Configuring secure container environments
  • Administering access controls and credentials
  • Safeguarding confidential ML assets

Production Deployment Strategies

  • Publishing images to container registries
  • Deploying containers in on-premises or cloud infrastructures
  • Managing versioning and updates for production services

Conclusion and Future Directions

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python or comparable programming languages
  • Comfort with fundamental Linux command-line operations

Target Audience

  • ML engineers responsible for model deployment in production
  • Data scientists seeking to manage reproducible experiment environments
  • AI developers creating scalable, containerized applications

Number of participants


Price per participant

Open Training Courses require 5+ participants.

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.

Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker

Upcoming Courses

Containerized AI & ML Deployment with Docker

2026-11-09 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Containerized AI & ML Deployment with Docker

2026-11-23 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-07 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-21 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Containerized AI & ML Deployment with Docker

2027-01-04 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

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