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 Duration 14 hours

Course Outline

Foundations of Containerization for AI & ML

  • Fundamental principles of containerization
  • The suitability of containers for ML workloads
  • Distinguishing containers from virtual machines

Managing Docker Images and Containers

  • Exploring images, layer structures, and registries
  • Utilizing containers for ML experimentation
  • Efficient usage of the Docker CLI

Encapsulating ML Environments

  • Readying ML codebases for containerization
  • Handling Python environments and dependencies
  • Incorporating CUDA and GPU support

Creating Dockerfiles for Machine Learning

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

Containerizing ML Models and Pipelines

  • Packaging trained models within containers
  • Implementing data and storage strategies
  • Executing reproducible end-to-end workflows

Operationalizing Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services using Docker Compose
  • Monitoring runtime performance

Security and Compliance Frameworks

  • Securing container configurations
  • Controlling access and credential management
  • Safeguarding confidential ML assets

Production Deployment Strategies

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

Conclusion and Future Directions

Requirements

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

Target Audience

  • ML engineers responsible for model deployment to production
  • Data scientists requiring reproducible experiment environments
  • AI developers engineering 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

2026-12-01 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-15 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-29 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

Containerized AI & ML Deployment with Docker

2027-01-12 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

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