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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 containers from virtual machines

Utilizing Docker Images and Containers

  • Comprehending images, layers, and registries
  • Overseeing containers for ML experimentation
  • Efficiently leveraging the Docker CLI

Encapsulating ML Environments

  • Preparing ML codebases for containerization
  • Administering Python environments and dependencies
  • Incorporating CUDA and GPU capabilities

Creating Dockerfiles for Machine Learning

  • Architecting Dockerfiles for ML projects
  • Best practices for enhancing performance and maintainability
  • Implementing multi-stage builds

Containerizing ML Models and Pipelines

  • Encapsulating trained models within containers
  • Strategizing data management and storage
  • Deploying consistent end-to-end workflows

Executing Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services via Docker Compose
  • Monitoring runtime behavior

Security and Compliance Strategies

  • Establishing secure container configurations
  • Managing access controls and credentials
  • Safeguarding confidential ML assets

Rolling Out to Production Environments

  • Releasing images to container registries
  • Implementing containers in on-prem or cloud infrastructures
  • Versioning and maintaining 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 focused on maintaining reproducible experimental 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

2026-11-23 09:30
14 hours
Tuxtla Gutiérrez - Santa Elena
76,450 MXN (Online)
96,450 MXN (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-07 09:30
14 hours
Ciudad Juárez - Bulevar Tomás Fernández
76,450 MXN (Online)
96,450 MXN (Classroom)

Containerized AI & ML Deployment with Docker

2026-12-21 09:30
14 hours
Toluca - La Merced
76,450 MXN (Online)
96,450 MXN (Classroom)

Containerized AI & ML Deployment with Docker

2027-01-04 09:30
14 hours
Toluca - Samara Shops Tower A
76,450 MXN (Online)
96,450 MXN (Classroom)

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