This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on Infrastructure for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand how Docker and Kubernetes help run machine learning tasks in a safe and repeatable way.
✓ Learn to use Infrastructure as Code (IaC) tools like Terraform to create and manage your ML environment.
✓ Work with Helm and Kustomize to organize, deploy, and update ML services inside Kubernetes.
✓ Explore how to set up storage, compute, and networks to support ML training and model serving at scale.
There are 10 modules in this course
- Introduction
- Containerization with Docker
- Kubernetes for MLOps
- Infrastructure as Code (IaC)
- Kubernetes Configuration Management
- Storage, Data Access, and Compute
- Streaming and Messaging Systems
- Local Development Infrastructure
- Infrastructure Design Patterns for MLOps
- Capstone Project
About this course
In this course, you will learn how to build and manage the infrastructure needed to run Machine Learning systems in production. While data scientists create models, MLOps Engineers are the ones who make sure those models run safely, reliably, and at scale. Good infrastructure helps teams move faster, avoid errors, and save time and money.
We will start with the core ideas of ML infrastructure – what it is, why it matters, and how it supports the full machine learning process. You will learn how to set up the systems that run ML tasks, serve models, and handle resources like storage, compute, and networks. Step by step, we will explore how to organize and automate these parts to make everything more repeatable, stable, and easy to control.
This course is not about training models. It is about everything that happens around the models – how to run them in different environments, how to prepare systems for new versions, and how to keep everything simple and predictable as projects grow. You will also learn how MLOps teams structure their work to support both small experiments and large-scale ML operations.
By the end of the course, you will understand how infrastructure supports MLOps work in real life. You will be ready to move forward with more focused topics like automation, CI/CD, observability, or scaling GenAI systems.
The goal of this course is to help you think like an MLOps Engineer – someone who builds the systems that keep ML running every day. Whether you’re starting your career or improving your team’s workflows, this course gives you a strong, practical foundation.
Start learning high demand tech skills today
Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.
In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.
You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.
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