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 Duration 21 hours

Course Outline

Containerization Basics for MLOps

  • Analyzing the requirements of the ML lifecycle
  • Essential Docker concepts applicable to ML systems
  • Best practices for creating consistent environments

Constructing Containerized ML Training Pipelines

  • Bundling model training code and its dependencies
  • Setting up training jobs via Docker images
  • Handling datasets and artifacts within containers

Containerizing Validation and Model Assessment

  • Recreating consistent evaluation environments
  • Streamlining validation workflows through automation
  • Recording metrics and logs from containerized processes

Containerized Inference and Serving

  • Designing microservices for inference
  • Optimizing runtime containers for production use
  • Implementing scalable serving architectures

Orchestrating Pipelines with Docker Compose

  • Managing multi-container ML workflows
  • Handling environment isolation and configuration
  • Connecting auxiliary services (such as tracking and storage)

Managing ML Model Versions and Lifecycle

  • Monitoring models, images, and pipeline elements
  • Maintaining version-controlled container environments
  • Integrating tools like MLflow or alternatives

Deploying and Scaling ML Workloads

  • Executing pipelines in distributed settings
  • Scaling microservices using Docker-specific methods
  • Monitoring performance in containerized ML systems

Implementing CI/CD for MLOps with Docker

  • Automating the build and deployment of ML components
  • Testing pipelines in containerized staging environments
  • Guaranteeing reproducibility and rollback capabilities

Wrap-up and Future Steps

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python for data analysis or model development
  • Basic knowledge of container fundamentals

Target Audience

  • MLOps Engineers
  • DevOps Professionals
  • Data Platform Teams

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

Docker for MLOps: End-to-End Pipeline Containerization

2026-11-16 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Docker for MLOps: End-to-End Pipeline Containerization

2026-11-30 09:30
21 hours
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36,896,260 UZS (Classroom)

Docker for MLOps: End-to-End Pipeline Containerization

2026-12-14 09:30
21 hours
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33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Docker for MLOps: End-to-End Pipeline Containerization

2026-12-28 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Docker for MLOps: End-to-End Pipeline Containerization

2027-01-11 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

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