This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on MLOps Best Practices Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Learn how to organize ML projects with clear structure, naming rules, and simple tools that help teams work together smoothly.
✓ Set up repeatable training and deployment steps using Git, CI/CD, and workflow tools like MLflow and Airflow.
✓ Understand how to monitor ML systems in production, find problems early, and improve models with feedback from real users.
✓ Follow best practices for testing ML code, keeping models safe, and saving costs in training and serving stages.
There are 13 modules in this course
- Introduction
- Reproducibility & Experiment Management
- Environment & Dependency Management
- Collaboration & Team Practices
- Model & Pipeline Design Principles
- Testing & Quality Assurance for ML
- Monitoring & Continuous Improvement
- CI/CD and Automation Best Practices
- Cost Efficiency and FinOps Culture
- Responsible AI & Governance
- Templates, Tools, and Frameworks
- Patterns & Anti-Patterns
- Capstone Project
About this course
In this course, you will learn how to build and run Machine Learning systems the right way. Many teams can train a model, but they often struggle when they try to move that model into production and keep it working over time. That’s where best practices come in – to make ML operations more stable, safe, and easy to manage.
We will look at what good MLOps work looks like in real projects. You will learn how to keep code, data, and models organized, how teams work together on ML systems, and how to test and monitor your ML pipelines. This course is not about building models – it’s about how to run them well and keep them running.
You will also understand what goes wrong when best practices are missing: broken pipelines, hidden bugs, slow feedback, and wasted time. We will talk about how to avoid these problems by following simple rules and setting up smart processes that can grow with your team and project.
By the end of the course, you will have a full set of practical skills and ideas that help you work like a professional MLOps Engineer. This course is for people who already know what MLOps is and want to go one step further – to learn how to do it better, with less risk and more control.
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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