MLOps for Beginners

About this course

MLOps for Beginners is Microsoft's practical introduction to Machine Learning Operations — the discipline of managing ML models from development through production. You'll learn how to move models from notebooks into real-world systems, keep them running reliably, and monitor their performance over time. This course bridges the gap between data science and engineering, a skillset that Microsoft and industry leaders increasingly demand.

What you'll learn

• Set up reproducible ML pipelines and version control for models and data
• Deploy machine learning models to production environments safely and reliably
• Monitor model performance in production and detect when retraining is needed
• Implement CI/CD (continuous integration and continuous deployment) for ML workflows
• Use containerization and cloud platforms to scale ML applications
• Troubleshoot common production issues and establish best practices for team collaboration
• Build end-to-end ML systems that are maintainable, testable, and reproducible

Who this is for

You're ready for this course if you've built a few machine learning models but want to understand how to ship them professionally. Whether you're transitioning from data science to engineering, joining a startup's ML team, or preparing for your first MLOps role, this curriculum will show you the real-world workflows used in production.

• Data Scientists & ML Engineers — move beyond notebooks and deploy models that create actual business value in production systems.
• Software Engineers moving into ML — learn the operational practices that make ML reliable, scalable, and maintainable at enterprise scale.

Prerequisites

Familiarity with Python and the basics of machine learning (what training, validation, and testing mean). You don't need to be an expert — the course assumes you've completed at least one end-to-end ML project. If you're comfortable with pandas, scikit-learn, or similar libraries, you're ready to start.

Why this matters for Indian learners

India's AI and ML job market is growing fastest in roles that bridge data science and engineering. Companies like Flipkart, Amazon, Swiggy, and Unacademy are scaling ML teams and actively hiring for MLOps and ML engineering roles — often with salaries 30–50% higher than pure data science positions. As India builds its own AI infrastructure and scales ML adoption across fintech, e-commerce, and edtech, the ability to deploy and maintain models in production is becoming essential, not optional.

Frequently asked questions

Is this course really free?

Yes. Microsoft hosts this entire curriculum on GitHub at no cost. You can access all lectures, code examples, and assignments freely.

How long will it take to complete?

Expect around 12 hours of active learning. If you're studying part-time, that's roughly 3–4 hours per week over a month, or you can go faster if you dive in full-time.

Will I get a certificate?

This course does not offer a formal completion certificate. However, you'll build a portfolio of real MLOps projects you can showcase to employers.