Top 7 Udemy Courses to Learn MLOps and AIOps in 2027

My favorite Udemy courses to learn MLOps, LLMOps, and AIOps in 2026

Hello guys, As artificial intelligence becomes the foundation of modern software, the ability to deploy, monitor, and manage AI systems at scale is becoming a must-have skill for every data scientist, ML engineer, and DevOps professional.

That’s where MLOps and AIOps come in — bridging the gap between machine learning development and real-world production operations.

By 2026, companies won’t just be hiring people who can train models. They’ll want engineers who can integrate LLMs, automate retraining, manage pipelines, and ensure observability across complex AI systems.

Fortunately, you can now learn these skills online with top-rated Udemy courses that combine practical labs, end-to-end projects, and real-world cloud experience.

Earlier, I have shared best AI courses, best ChatGPT courses, best Data Science courses and best Machine Learning courses and in this article, I am going to share best Udemy courses to learn MLOps, AIOps, and LLOps in 2026.

You can join one or more of these courses to get yourself familiar of running AI model and AI agents in production. The interaction of AI infra, Cloud, and Machine Learning is an exciting field these days.

So what are we waiting for, let’s start now

7 Best Udemy Courses to Learn MLOps, AIOps, and LLMOps in 2026

Without any further ado, here are 7 best Udemy courses to learn MLOps and AIOps in 2026, covering everything from CI/CD for ML to LLMOps, RAG pipelines, and cloud deployment across AWS, GCP, and Azure.

1. LLMOps and AIOps Bootcamp with 8 End-to-End Projects

Instructor: Expert-led team | Level: Intermediate to Advanced

If you’re serious about deploying Large Language Models (LLMs) and want hands-on experience in AIOps, this bootcamp is a must. It covers Jenkins CI/CD, Docker, Kubernetes, AWS/GCP, Prometheus monitoring, and vector databases for real-world AI deployment. T

The course includes 8 full projects simulating production-grade environments — helping you understand how to operationalize GenAI systems securely and efficiently.

What sets this course apart is the emphasis on end-to-end pipelines, observability, and post-deployment maintenance, making it ideal for engineers transitioning from ML to full-scale production AI systems.

Here is the link to join this course — LLMOps and AIOps Bootcamp with 8 End-to-End Projects

2. Complete MLOps Bootcamp with 10+ End-to-End ML Projects

Instructor: Experienced ML Engineer | Level: All Levels

This course is one of the most comprehensive on Udemy for mastering MLOps fundamentals. It includes over 10 hands-on projects covering every stage of the ML lifecycle — from data ingestion and model training to deployment and automation.

You’ll learn how to integrate MLflow, Docker, Jenkins, and Kubernetes, and how to manage versioning, reproducibility, and continuous delivery in ML environments.

The course also provides a strong foundation for engineers aiming to move into ML engineering and production AI roles.

Here is the link to join this course — Complete MLOps Bootcamp with 10+ End-to-End ML Projects

3. AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale

Instructor: Senior AI Engineer | Level: Intermediate to Advanced

Designed for AI engineers building Generative AI and Agentic AI systems, this course focuses on scaling AI workloads across AWS, GCP, Azure, and Vercel.

It also introduces MCP (Model Context Protocol), Bedrock, and SageMaker, helping you manage large-scale, multi-model deployments.

If you’re interested in AI infrastructure, LLM deployment, or enterprise AI pipelines, this track provides an exceptional foundation for designing scalable and observable systems that support cutting-edge AI applications.

Here is the link to join this course — AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale

4. Beginner to Advanced MLOps on GCP: CI/CD, Kubernetes, Jenkins

Instructor: Cloud AI Expert | Level: Beginner to Advanced

For those who prefer the Google Cloud ecosystem, this course teaches you how to build and automate ML pipelines using Kubernetes, GitLab CI, Jenkins, Prometheus, Grafana, Kubeflow, and Minikube.

You’ll gain hands-on experience with real-world GCP workflows, including data preprocessing, containerization, and continuous integration for ML models.

It’s a great way to build strong cloud-native MLOps skills while understanding how GCP simplifies large-scale AI deployment.

Here is the link to join this course — Beginner to Advanced MLOps on GCP: CI/CD, Kubernetes, Jenkins

5. Ultimate DevOps to MLOps Bootcamp — Build ML CI/CD Pipelines

Instructor: Full Stack ML Engineer | Level: Intermediate

If you already understand DevOps and want to transition into MLOps, this course bridges the gap perfectly.

You’ll build a real-world ML project using MLflow, Docker, and Kubernetes, and deploy it with a CI/CD pipeline that simulates enterprise operations.

The course emphasizes collaboration between data scientists, ML engineers, and DevOps teams, showing how to create efficient pipelines for model retraining, performance tracking, and automation — exactly what modern AI teams need.

Here is the link to join this course — Ultimate DevOps to MLOps Bootcamp — Build ML CI/CD Pipelines

6. MLOps with AWS — Bootcamp: Zero to Hero Series

Instructor: AWS Certified Instructor | Level: Beginner to Intermediate

If your goal is to master MLOps on AWS, this bootcamp covers everything from Jupyter notebooks to production-ready ML operations.

You’ll learn Amazon SageMaker, CloudWatch, ECS, and ECR, and practice building end-to-end pipelines that integrate seamlessly with the AWS ecosystem.

The “Zero to Hero” approach ensures you build confidence step-by-step — ideal for those transitioning from data science or DevOps backgrounds into cloud-based MLOps roles.

Here is the link to join this course — MLOps with AWS — Bootcamp: Zero to Hero Series

7. Azure Machine Learning & MLOps: Beginner to Advance

Instructor: Microsoft Certified Trainer | Level: Beginner to Advanced

This course is perfect for developers and engineers working in Microsoft’s Azure environment. It provides a complete overview of Azure Machine Learning, MLOps, and model deployment automation.

You’ll learn how to use Azure ML Studio, Pipelines, and Monitoring tools to build scalable and cost-effective AI systems.

The course is project-based, helping you develop skills that directly translate to enterprise-level applications.

Here is the link to join this course — Azure Machine Learning & MLOps: Beginner to Advance

Final Thoughts

That’s all about the best Udemy courses to learn MLOps, AIOps and LLMOps in 2026. By 2027, mastering MLOps and AIOps will be one of the fastest ways to advance your career in AI engineering.

Whether you’re working on LLMs, traditional ML pipelines, or GenAI systems, these Udemy courses offer the best blend of hands-on learning, cloud integration, and production-level experience.

If you’re serious about staying ahead in AI infrastructure, start with any of these bootcamps and build real-world projects that showcase your ability to operationalize AI at scale.

By the way, if you want to join multiple courses on Udemy then you can also checkout Udemy’s Personal Plan, where you get access to best of Udemy’s 11000+ courses for a monthly fee of $30.

If you want to join multiple courses then Udemy Personal Plan is actually a better deal. You can also try for free for 7 days to get a feel of it.

So, what are you waiting for? Pick a course, start learning, and join the AI revolution!

Happy Learning!

Thanks for reading this article so far. If you find these Udemy Courses for learning MLOps, AIOps, and LLMOps then please share with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. — If you are a complete beginner on Agentic AI then I also recommend you to first go through a comprehensive course like The Complete Agentic AI Engineering (2025) Course, I highly recommend that to anyone who want to start with Agentic AI.

I Tried 50 Artificial Intelligence Courses: Here Are My Top 5 Recommendations for 2025

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