Best MLOps Courses on Udemy: A Practical Guide for Learners in 2025

Are you looking for the Best MLOps Courses on Udemy to learn how to take your machine learning models from notebooks to real-world use? If yes, you’re in the right place. When I first started working with machine learning models, the biggest challenge wasn’t training them; it was what came after. Deploying models, maintaining pipelines, monitoring performance, and making sure everything scaled reliably in production was a completely different game. That’s where MLOps (Machine Learning Operations) comes in.

Over the last few years, I’ve seen MLOps move from being a “nice to have” skill to an absolute requirement for data scientists and ML engineers. Companies don’t just want working models; they want models that can be deployed, monitored, and updated seamlessly.

Udemy has become one of the go-to platforms for learning these skills, mainly because of its hands-on courses and affordability compared to boot camps or specialized certifications. I’ve gone through and analyzed the top MLOps courses on Udemy, and in this blog, I’ll share which ones are worth your time depending on your goals.

Best MLOps Courses on Udemy

What to Know Before Starting an MLOps Course

Before jumping into an MLOps course, it’s good to have a few basics covered:

• Python – Most courses expect you to know at least some Python.
• Machine Learning basics – You should have an idea of how models are trained and used.
• Docker (just the basics) – Not always required, but really helpful to understand.
• Cloud platforms – Some courses use AWS or Azure, so you may need a free account.

If you don’t know all of these yet, that’s completely fine. You can still start the course, just keep in mind that you might need to spend a little extra time learning the basics along the way.

1. Azure Machine Learning & MLOps (Beginner to Advanced)

If you’re planning a career in Azure Cloud + MLOps, this course is as close to a complete roadmap as it gets.

• Content length: ~20 hours
• What you’ll learn: CI/CD pipelines with Azure DevOps, deployment on Kubernetes, GitHub Actions, Databricks integration, and Responsible AI
• Why it’s useful: Unlike many cloud-focused courses, this one is very practical. You actually build pipelines and deploy models, rather than just hearing theory.
• Requirement: You’ll need an Azure account for hands-on practice.

My take: If your current or future role is in a company that uses Azure, this course is a strong investment. It prepares you to work directly on production-grade workflows.

2. MLOps Fundamentals

This course is short and beginner-friendly, perfect if you’re just getting started with MLOps.

• Content length: ~3 hours
• What you’ll learn: MLOps maturity levels, challenges in traditional ML lifecycles, CI/CD basics, and a simple end-to-end pipeline demo in Azure
• Why it’s useful: It gives you a clear foundation in concepts without overwhelming you.
• Who it’s for: Students, freshers, or data scientists who want a quick orientation before diving into cloud-heavy or tool-heavy content.

My take: If you’re completely new to MLOps and unsure where to begin, this course helps you get clarity fast. Think of it as a “starter pack” before you commit to longer bootcamps.

3. Deployment of Machine Learning Models

This one is all about deployment best practices — a step often overlooked by data scientists.

• Content length: 10+ hours
• What you’ll learn: API building, Docker packaging, CI/CD with CircleCI, reproducibility, testing, logging, versioning, AWS ECS deployment
• Why it’s useful: You go beyond notebooks and learn how to set up production systems that teams can actually use.
• Focus area: More tool-agnostic and not tied to one cloud platform.

My take: If you’ve been comfortable building models in Jupyter but now want to understand how real companies deploy them, this course bridges that gap.

4. Complete MLOps Bootcamp by Krish Naik

This is one of the most popular and comprehensive MLOps courses on Udemy, created by Krish Naik.

• Content length: 50+ hours
• What you’ll learn: Git, Docker, MLflow, DVC, Airflow, AWS SageMaker, Azure, HuggingFace, Grafana monitoring
• Highlight: 10+ end-to-end real-world projects covering multiple tools and platforms
• Why it’s useful: Project-based learning ensures you don’t just learn theory — you apply it repeatedly.

My take: If you’re serious about a career in MLOps and willing to put in the time, this course gives you the most well-rounded and practical experience. But it’s also a big-time commitment, so be prepared to invest weeks, not just hours.

5. MLOps Bootcamp: Mastering AI Operations for Success

A relatively new course, but very promising because it combines MLOps with AIOps (AI for IT Operations).

• Content length: 36+ hours
• What you’ll learn: Python for MLOps, Git, MLflow, Docker, FastAPI, Prometheus, Grafana, WhyLogs for monitoring
• Why it’s useful: Focuses not just on deployment but also on maintaining and monitoring ML systems at scale.
• Who it’s for: Learners who want to cover both deployment and operational monitoring.

My take: If you’re particularly interested in the monitoring and scaling side of MLOps (which is becoming more important as AI systems go live), this course is a solid choice.

So, Which One Should You Pick?

This is a simple decision framework based on your goals:

• Want Azure-specific skills → choose Azure Machine Learning & MLOps.
• Need a quick introduction → start with MLOps Fundamentals.
• Focused on deployment best practices → take Deployment of Machine Learning Models.
• Want projects and full coverage → Complete MLOps Bootcamp by Krish Naik is the most comprehensive.
• Interested in deployment plus monitoring at scale → MLOps Bootcamp: Mastering AI Operations for Success is a strong option.

Quick Comparison Table

Course Name	Duration	What It Covers	Best For
Azure Machine Learning & MLOps	~20 hrs	Azure DevOps, CI/CD, Kubernetes	Learners who want to work with Azure tools
MLOps Fundamentals	~3 hrs	Basics, CI/CD, ML lifecycle	Beginners just getting started
Deployment of ML Models	10+ hrs	Deployment with Docker, CircleCI, AWS ECS	Learners focusing on model deployment
Complete MLOps Bootcamp (Krish Naik)	50+ hrs	Full coverage with 10+ projects, AWS/Azure	Serious learners who want hands-on mastery
MLOps Bootcamp: Mastering AI Ops	36+ hrs	Deployment + monitoring (Prometheus, Grafana)	Students interested in deployment + monitoring skills

Career Benefits of Learning MLOps

After finishing one (or more) of these MLOps courses, here are some roles you can aim for:

• MLOps Engineer – Take care of pipelines, CI/CD, and keep ML systems running smoothly.
• Machine Learning Engineer – Focus on getting models into production and making sure they scale well.
• Data Engineer (with MLOps skills) – Build strong data pipelines that work seamlessly with ML workflows.
• AI Engineer – Work on both the modeling side and the operations side of end-to-end AI projects.

These skills are in high demand across industries like finance, healthcare, e-commerce, and SaaS companies—so you’ll have plenty of opportunities once you build them.

Tips for Choosing the Right Course

• Time commitment: Think about how much time you can give. Do you just want a short 3-hour introduction, or are you ready for a longer 50-hour bootcamp?
• Learning style: Some learners enjoy building projects step by step, while others like to start with clear explanations before trying things out. Go with what makes learning easier for you.
• Career path: If you want to work with cloud tools like Azure, pick a course that focuses on that. If your goal is general deployment skills, choose a course that covers the broader picture.
• Budget and pace: Udemy has frequent sales, so wait for a discount and get the course that fits your needs without spending extra.

Do I need coding skills for MLOps?

Yes. Most MLOps work is done in Python. You don’t have to be a pro, but knowing the basics of coding will really help.

Can I learn MLOps without cloud knowledge?

Yes, you can start without it. Some courses don’t depend on any specific cloud tools. But later on, learning platforms like AWS, Azure, or GCP will make you much stronger in MLOps.

Are Udemy MLOps certificates recognized?

They are not the same as official certifications from big providers like AWS or Azure. Still, Udemy certificates show that you’ve learned practical skills, and what really matters to employers is the projects you can showcase.

How do I practice after finishing a course?

You can deploy your own models as APIs, try contributing to open-source projects on GitHub, or use the free cloud tiers from providers to test your pipelines.

Which course should I start with as a beginner?

If you are just starting out, go for a short and simple course like MLOps Fundamentals. Once you are comfortable with the basics, you can move on to longer bootcamps that include projects.

Conclusion

Learning MLOps isn’t about memorizing tools; it’s about understanding how to take machine learning models from research to production in a reliable way. The Best MLOps Courses on Udemy cover this journey from different angles — some focus on cloud platforms like Azure, some on the basics and fundamentals, while others go deeper into deployment best practices and monitoring at scale.

My advice is to be clear about your current stage and your career goals. If you’re a beginner, don’t start with a 50-hour bootcamp. Begin with one of the shorter Udemy MLOps courses that gives you the foundations. If you already have experience in ML, go for a more advanced course that provides hands-on deployment and end-to-end projects.

From my own experience, learning MLOps has opened doors — not just to better job opportunities but also to solving real-world problems more effectively. The good thing is, with Udemy MLOps courses, you can learn at your own pace and practice directly with the tools used in the industry. If you stay consistent, these courses can give you the right skills to grow your career and build production-ready ML systems.

Happy Learning!

Thank YOU!

Thought of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford