MLOps Bootcamp (Krish Naik)

About this course

This bootcamp teaches you how to take machine learning models from experimentation to production—the hardest part most data scientists skip. Krish Naik, a trusted figure in India's ML community, walks you through real-world deployment using industry tools like MLflow, Docker, AWS, and CI/CD pipelines. Whether you're building hobby projects or aiming for data engineering roles, this course closes the gap between "model that works on my laptop" and "model that actually runs in production."

What you'll learn

• Set up MLflow to track experiments, log models, and manage versions—essential for working in teams
• Containerize ML applications with Docker so they run the same way on your machine, your colleague's machine, and production servers
• Deploy models to AWS (EC2, S3, SageMaker) and understand cloud costs and scaling
• Build CI/CD pipelines that automatically test, build, and deploy your code when you push to GitHub
• Handle real-world challenges: managing dependencies, environment variables, monitoring model performance in production
• Integrate databases and APIs to make your models accessible to other applications
• Debug production failures and keep models running smoothly over time

Who this is for

You're ready for this course if you've built at least a few machine learning models and want to stop wondering how they actually get used in the real world. You don't need to be a DevOps expert—just curious and willing to learn by doing.

• Data scientists—move beyond notebooks and learn the engineering skills that make you hireable for senior roles
• ML engineers—master the deployment and production tools that separate junior engineers from the people who actually ship

Prerequisites

You should have working knowledge of Python and have built at least one or two machine learning models (even simple ones like a regression or classifier). Familiarity with the command line helps. You don't need DevOps experience.

Why this matters for Indian learners

India's AI and data job market is shifting fast. Startups and enterprises across Bangalore, Hyderabad, and Mumbai increasingly need people who can deploy models, not just build them. Companies like Flipkart, Amazon India, and consulting firms hiring for ML roles explicitly want engineers who understand production deployment. MLOps skills put you in the top 20% of candidates and often translate to 20–30% higher salaries in tier-1 cities. Learning these tools now—for free—is a practical bet on your career.

Frequently asked questions

Is this course really free?

Yes. It's a free YouTube playlist. No hidden paywalls, no "pay for the certificate" upsell. You get everything.

How long will it take to complete?

The full playlist is roughly 30 hours. That translates to about 5–7 weeks if you spend 5 hours a week, or 2–3 weeks if you go faster. Don't rush—MLOps is hands-on, and actually following along with the code is where the learning sticks.

Will I get a certificate?

No formal certificate. But the real payoff is building projects you can show on GitHub or in interviews. Your portfolio of deployed models matters far more than a certificate in this field.