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 Duration 14 hours

Course Outline

Introduction to Containerisation for AI and ML

  • Core principles of containerisation
  • The suitability of containers for ML workloads
  • Key distinctions between containers and virtual machines

Working with Docker Images and Containers

  • Understanding images, layers, and registries
  • Managing containers for ML experimentation
  • Efficient use of the Docker CLI

Packaging ML Environments

  • Preparing ML codebases for containerisation
  • 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
  • Utilising multi-stage builds

Containerising ML Models and Pipelines

  • Packaging trained models into containers
  • Managing data and storage strategies
  • Deploying reproducible end-to-end workflows

Running Containerised ML Services

  • Exposing API endpoints for model inference
  • Scaling services with Docker Compose
  • Monitoring runtime behaviour

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

  • A solid grasp of machine learning workflows
  • Experience with Python or comparable programming languages
  • Basic familiarity with Linux command-line operations

Target Audience

  • ML engineers responsible for deploying models to production
  • Data scientists managing reproducible experiment environments
  • AI developers building scalable, containerised 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

Provisional Upcoming Courses (Require 5+ participants)

Containerized AI & ML Deployment with Docker

2026-11-17 09:30
14 hours
Te Pou Toetoe Linwood
5480 NZD (Online)
12980 NZD (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-01 09:30
14 hours
Auckland, ANZ Centre
5480 NZD (Online)
14280 NZD (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-15 09:30
14 hours
Wellington, Plimmer Towers
5480 NZD (Online)
12980 NZD (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-29 09:30
14 hours
Te Pou Toetoe Linwood
5480 NZD (Online)
12980 NZD (Classroom)

Containerized AI & ML Deployment with Docker

2027-01-12 09:30
14 hours
Auckland, ANZ Centre
5480 NZD (Online)
14280 NZD (Classroom)

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