If you’ve been sitting on the MLOps learning fence, 2026 is the year to jump off it. Glassdoor reports the average MLOps engineer salary hitting $161,317 per year in the US, with top performers clearing well above $200K. Meanwhile, LinkedIn data shows MLOps has seen 9.8× growth in just five years — making it one of the fastest-moving careers in all of tech.
The problem? Most machine learning professionals know how to build models, but very few know how to actually run them in production. That gap — between notebook experiments and live, monitored, scalable systems — is exactly what MLOps closes. And Udemy has become one of the most affordable places to close that gap fast — especially when you grab courses through Udemy Coupon Code deals.
We put in the hours reviewing the Udemy MLOps catalog this February 2026, filtering for courses that are freshly updated, project-heavy, and taught by instructors who’ve actually shipped ML systems. Here are the eight that made the cut.
What Is MLOps and Why Does It Matter in 2026?
MLOps (Machine Learning Operations) is the practice of bringing DevOps discipline — automation, versioning, monitoring, CI/CD — into the machine learning lifecycle. Without MLOps, most ML projects stall out after the model training phase. With it, teams can deploy, monitor, retrain, and scale models reliably in production.
The MLOps market is expected to grow from $1.1 billion in 2022 to $5.9 billion by 2027 at a 41% CAGR. Companies aren’t just hiring data scientists anymore — they want engineers who can operationalize AI at scale, handle model drift, build reproducible pipelines, and integrate ML into cloud infrastructure.
MLOps sits right at the intersection of three booming disciplines: machine learning, cloud infrastructure, and DevOps automation. If you’re already working in any of those areas, pivoting toward MLOps is arguably the highest-ROI career move you can make this year. And if you’re curious about how AI agents fit into modern MLOps pipelines, the best Udemy courses on Agentic AI for 2026 are worth checking out alongside this list.
How We Selected These Courses
Before recommending anything, we applied strict filters across the entire Udemy MLOps catalog. This list was last verified and updated February 2026:
- Rating: ≥ 4.0 stars with a minimum of 500 student enrollments
- Freshness: Updated within the last 4 months, with new content on LLMOps, advanced monitoring, and modern tooling
- Content depth: At least 70% of course hours dedicated to MLOps and production deployment — not just introductory ML theory
- Hands-on projects: Real CI/CD pipelines, cloud deployments, or end-to-end ML workflows included
- Instructor quality: Active Q&A support and verifiable production experience
- Price: Available at steep discount during Udemy sales, regularly under $20
Eight courses met all criteria. Here’s the full breakdown.
MLOps vs LLMOps in 2026: What Changed?
Traditional MLOps focused on deploying static models (regression, classification) with tools like SageMaker and Kubeflow. In 2026, the majority of production AI work involves Large Language Models (LLMs) and agentic systems.
LLMOps adds new challenges:
- Prompt drift & versioning
- Token cost optimization
- Agent orchestration (LangGraph, CrewAI)
- RAG pipeline monitoring
- Tools: LangSmith, Phoenix, TruLens, Helicone, LiteLLM
The best courses on this list now include LLMOps coverage (#4 & #6), while traditional MLOps courses (#1, #2, #8) remain essential foundations.
8 Best Udemy MLOps Courses for 2026
1. MLOps Zero to Hero — Abhishek Veeramalla
Best for: DevOps engineers transitioning into production-grade MLOps with AWS and Kubernetes.
This one lands at the top of our list for a reason. Abhishek Veeramalla doesn’t teach MLOps like a textbook — he teaches it the way it actually works inside engineering teams. You start with the ML lifecycle basics, but the course moves quickly toward the production challenges that actually matter: data and model versioning with DVC, experiment tracking with MLflow, containerizing models with Docker, and orchestrating workloads on Kubernetes using KServe.
What sets this course apart is the AWS integration. You get real hands-on time with Amazon SageMaker and Kubeflow — the tools enterprise teams are running right now. Every concept ties back to a real workflow rather than a hypothetical diagram, which is exactly what you need if you’re planning to transition from a DevOps role into MLOps.
If you’ve worked with Docker and Kubernetes before, you’ll find this course moves at a satisfying pace. If you’re newer to the infrastructure side, the explanations are clear enough that you’ll keep up without frustration. For those also exploring AWS AI services in depth, the best AWS AI courses on Udemy for 2026 make an excellent companion to this one.
What you’ll learn:
- ML Operations fundamentals and the full ML lifecycle
- Transitioning your DevOps skills into MLOps responsibilities
- Machine learning basics for infrastructure-focused engineers
- Model deployment, serving patterns, and production monitoring
- End-to-end ML pipeline orchestration with Kubeflow
- Real-world MLOps project from scratch
Who this is for: DevOps engineers moving into MLOps, anyone curious about how ML models are managed at production scale, beginners interested in model deployment and maintenance.
Enrollment: 5,000+ students | Rating: 4.7/5 | Duration: 15+ hours | Sale Price: $15.99
→ Get MLOps Zero to Hero on Udemy
2. Ultimate DevOps to MLOps Bootcamp — Gourav J. Shah
Best for: Bridging DevOps and MLOps with a real end-to-end CI/CD project.
This bootcamp takes a single project — predicting house prices using a regression model — and uses it as the vehicle for teaching everything from Docker setup and MLflow experiment tracking to GitHub Actions CI pipelines, Kubernetes inference infrastructure, and GitOps-based continuous delivery with ArgoCD.
That single-project approach is the course’s biggest strength. Instead of context-switching between different examples, you always know where you are in the workflow. By the time you’ve deployed your model behind Seldon Core and set up Prometheus/Grafana monitoring dashboards, the entire MLOps pipeline clicks into place as a unified system rather than a pile of disconnected tools.
Gourav also covers the handoff workflows between data science, ML engineering, and DevOps teams — context that most courses skip entirely but that matters enormously inside a real organization.
What you’ll learn:
- End-to-end ML pipelines following MLOps best practices
- MLflow for experiment tracking and model versioning
- Model packaging with FastAPI and Docker
- GitHub Actions for automated CI pipelines
- Kubernetes inference infrastructure with KIND
- GitOps-based continuous delivery with ArgoCD
- Seldon Core for production model serving
- Prometheus and Grafana monitoring for live models
Who this is for: DevOps engineers breaking into MLOps, platform engineers supporting ML teams, cloud engineers wanting to understand production ML workflows.
Enrollment: 18,000+ students | Rating: 4.6/5 | Duration: 12+ hours | Sale Price: $16.99
→ Get Ultimate DevOps to MLOps Bootcamp on Udemy
3. Complete MLOps Bootcamp With 10+ End-To-End ML Projects — Krish Naik
Best for: Learners who want maximum hands-on project variety covering data science and MLOps together.
Krish Naik is one of the most followed ML educators on YouTube, and his Udemy bootcamp earns that reputation. With over 51 hours of content and more than 10 complete end-to-end projects, this is the most comprehensive MLOps course on this list — and honestly one of the most thorough available anywhere at this price point.
Projects span traditional ML, NLP with Hugging Face transformers, AWS SageMaker deployment, Generative AI on AWS, and pipeline automation with Apache Airflow and Astro. The course also goes into real-time model monitoring with Grafana connected to PostgreSQL — a level of depth that most MLOps courses never reach.
The tradeoff is scope. At 51 hours, this is a serious commitment. But if you want a single resource that takes you from Git and Docker basics all the way through multi-cloud deployment and GenAI infrastructure, treat this like your MLOps MBA. Pair it with the top AI integration courses on Udemy for 2026 to round out your production AI skill set.
What you’ll learn:
- Scalable MLOps pipelines with Git, Docker, and CI/CD integration
- MLflow and DVC for model versioning and experiment tracking
- End-to-end ML model deployment on AWS SageMaker
- ETL pipeline and ML workflow automation with Apache Airflow
- Grafana and PostgreSQL for production monitoring
- Generative AI deployment on AWS cloud infrastructure
- NLP project deployment using Hugging Face transformers
Who this is for: Data scientists and ML engineers wanting production deployment skills, DevOps professionals integrating ML pipelines, software engineers transitioning to MLOps, beginners with basic ML knowledge.
Enrollment: 31,000+ students | Rating: 4.5/5 | Duration: 51+ hours | Sale Price: $17.99
→ Get Complete MLOps Bootcamp on Udemy
4. LLMOps Masterclass 2026 — Generative AI, MLOps & AIOps — Manifold AI Learning
Best for: Professionals focused on deploying and operating Large Language Models in production.
Most MLOps courses were designed before Large Language Models became central to production AI systems. This one wasn’t. The LLMOps Masterclass is built around the reality that in 2026, a growing share of production ML work involves LLMs — and operating them requires a noticeably different skill set than managing traditional ML pipelines.
You’ll build LLM applications with ChatGPT and Hugging Face from scratch, package and deploy them using FastAPI, Docker, and Kubernetes, and implement CI/CD workflows built specifically for LLM-powered services. The course covers prompt engineering architecture and production monitoring for LLM models — areas that most competing courses treat as an afterthought, if they cover them at all.
If your work is shifting toward GenAI infrastructure, this course is a strong operational foundation. Want to go even deeper on running models locally without cloud costs? The best Ollama local AI courses on Udemy for 2026 are a natural complement — covering how to run LLMs offline and free, which many teams use for dev/test before pushing to production.
What you’ll learn:
- Generative AI fundamentals and real-world applications
- Prompt engineering architecture, components, and techniques
- Building LLM applications with ChatGPT and Hugging Face
- FastAPI, Docker, and Kubernetes for AI application deployment
- CI/CD pipelines using GitHub Actions for LLM services
- Production monitoring for LLM models in live environments
- LLMOps basics including version control and deployment workflows
Who this is for: AI enthusiasts, data scientists, software engineers building LLM-powered products, DevOps engineers expanding into AIOps, entrepreneurs building AI-first products.
Enrollment: 5,000+ students | Rating: 4.5/5 | Duration: 16+ hours | Sale Price: $14.99
→ Get LLMOps Masterclass on Udemy
5. Full-Stack AI Engineer 2026: ML, Deep Learning & Generative AI — School of AI
Best for: Beginners to intermediate learners who want a complete path from Python fundamentals to production GenAI.
If you’re earlier in your journey and want a single course that takes you from Python all the way through to production MLOps and Generative AI, the Full-Stack AI Engineer bootcamp from School of AI is worth the investment. At 33 hours, it’s substantial but not overwhelming — and the curriculum is organized well enough that you can enter at the section matching your current level without feeling lost.
The course covers NumPy, Pandas, Scikit-learn for ML, TensorFlow and PyTorch for deep learning, and then moves into MLOps territory with Git, DVC, Docker, MLflow, and CI/CD across AWS, GCP, and Azure. The GenAI section covers OpenAI GPT, Claude, and Gemini APIs with RAG pipeline implementation — genuinely advanced material that’s rarely found at this price point.
This is the most beginner-friendly entry point on this list while still delivering real production skills.
What you’ll learn:
- Python programming fundamentals for AI and ML
- Data science techniques with NumPy, Pandas, Matplotlib, and Seaborn
- ML model building with Scikit-learn for classification, regression, and ensembles
- Deep learning with TensorFlow and PyTorch including CNNs, RNNs, and LSTMs
- MLOps pipeline implementation with Git, DVC, Docker, MLflow, and CI/CD
- Generative AI and LLM applications using GPT, Claude, and Gemini with RAG pipelines
Who this is for: Aspiring AI engineers, beginners wanting a structured learning roadmap, software engineers transitioning into ML roles, students wanting to understand how modern LLMs are built and deployed.
Enrollment: 4,000+ students | Rating: 4.6/5 | Duration: 33+ hours | Sale Price: $18.99
→ Get Full-Stack AI Engineer 2026 on Udemy
6. AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale — Ed Donner
Best for: Engineers ready to deploy LLMs, AI agents, and agentic workflows to real cloud production environments.
With 20,000+ enrollments and the highest rating on this entire list at 4.8/5, Ed Donner’s course has built a reputation that’s hard to ignore. Students call it “the missing course in AI” — and after going through the curriculum ourselves, that framing makes total sense.
This isn’t a course about building demo apps. It’s about deploying production SaaS applications to Vercel, AWS, Azure, and GCP with proper cloud architecture: Lambda, S3, CloudFront, SQS, Route 53, App Runner, and API Gateway. You’ll integrate with Amazon Bedrock and SageMaker, build with GPT-5, Claude 4, and open-source models, and automate deployment rollouts with Terraform and GitHub Actions.
The agentic AI section is particularly strong. Multi-agent systems, agentic loops with Amazon Bedrock AgentCore, and the Model Context Protocol (MCP) are covered in real deployable depth. If AI agents are still new territory for you, the best Udemy courses on Agentic AI for 2026 give you great context before diving in here. This course also pairs very naturally with the best AWS AI courses on Udemy for anyone going deep on Bedrock and SageMaker deployments.
What you’ll learn:
- Deploying SaaS LLM applications to Vercel, AWS, Azure, and GCP
- Cloud architecture design with Lambda, S3, CloudFront, SQS, and API Gateway
- Amazon Bedrock and SageMaker integration at production scale
- Infrastructure automation with Terraform and GitHub Actions CD
- Enterprise-grade AI with observability, guardrails, and explainability
- Multi-agent systems and agentic loops with Amazon Bedrock AgentCore
- Model Context Protocol (MCP) for agentic AI deployments
Who this is for: Engineers excited about deploying GenAI and AI agents to production — entrepreneurs, enterprise engineers, and everyone in between.
Enrollment: 20,000+ students | Rating: 4.8/5 | Duration: 18+ hours | Sale Price: $16.99
→ Get AI Engineer MLOps Track on Udemy
7. MLflow in Action: Master the Art of MLOps Using MLflow — J Garg
Best for: Data scientists and ML engineers who want to go deep on MLflow for experiment tracking, model versioning, and registry management.
Most MLOps courses treat MLflow as one tool among many. This one treats it as the focus — and that depth of coverage is exactly what makes it valuable. MLflow is used across thousands of organizations from early-stage startups to Fortune 500 companies, and knowing how to use it properly is a genuine differentiator on any ML engineering resume.
J Garg walks through all four MLflow components: Tracking, Projects, Models, and Registry. You’ll learn the full range of logging functions for experiments, how to package models into different flavors for different deployment targets, and how to manage model versions in the Registry over time. The course closes with a complete end-to-end project deploying a model on AWS SageMaker with full MLflow integration.
If you’re already a data scientist or ML engineer and want to sharpen your MLOps fundamentals without sitting through 40+ hours of content, this 12-hour course is the most targeted option on the list.
What you’ll learn:
- MLOps fundamentals and the challenges of traditional ML lifecycle management
- MLflow’s four core components: Tracking, Models, Projects, and Registry
- Experiment tracking with logging for runs, artifacts, parameters, and metrics
- Model packaging into different deployment flavors for flexible integration
- Reproducible ML workflows using MLflow Projects
- Model version management and quality tracking with MLflow Registry
- End-to-end ML project deployment on AWS SageMaker using MLflow
Who this is for: Data scientists, machine learning engineers, MLOps engineers, operations engineers.
Enrollment: 8,000+ students | Rating: 4.5/5 | Duration: 12+ hours | Sale Price: $13.99
→ Get MLflow in Action on Udemy
8. Deployment of Machine Learning Models — Soledad Galli
Best for: Data scientists who want to understand production-ready model deployment architecture and best practices from the ground up.
With 43,000+ students enrolled — the highest enrollment on this entire list — Soledad Galli’s deployment course has been quietly building a reputation as one of the most reliable ML deployment resources on Udemy. There’s a clear reason so many data scientists have reached for it first.
The course focuses on the fundamentals of taking a trained model and making it available through APIs, cloud deployments, and automated CI/CD pipelines. Soledad puts a heavy emphasis on code quality: testable, version-controlled, reproducible production code that won’t fall apart six months after you ship it. She also digs into the common pitfalls that cause ML deployments to fail in practice — model architecture mismatches, dependency issues, environment drift — knowledge you’d normally only pick up from painful first-hand experience.
At $12.99, it’s the most affordable option on this list. If you’re deploying your first ML model to production, or want to understand the deployment fundamentals before jumping into more advanced orchestration tooling, start here. And if you’re looking to understand how modern AI tools connect into broader system architectures after completing it, the top AI integration courses on Udemy for 2026 are worth exploring as a natural next step.
What you’ll learn:
- Building machine learning model APIs and deploying to the cloud
- Sending and receiving prediction requests from deployed models
- Writing testable, version-controlled, and reproducible production code
- Creating continuous and automated CI/CD integrations for model deployment
- Understanding optimal machine learning deployment architecture
- Identifying and mitigating common production deployment challenges
Who this is for: Data scientists deploying their first production model, engineers wanting ML deployment best practices, software developers transitioning into machine learning.
Enrollment: 43,000+ students | Rating: 4.5/5 | Duration: 10+ hours | Sale Price: $12.99
→ Get Deployment of Machine Learning Models on Udemy
Quick Comparison: All 8 MLOps Courses at a Glance
| # | Course | Instructor | Students | Rating | Hours | Best For | Price |
|---|---|---|---|---|---|---|---|
| 1 | MLOps Zero to Hero | Abhishek Veeramalla | 5k+ | ⭐ 4.7 | 15+ | Production MLOps with AWS/K8s | $15.99 |
| 2 | Ultimate DevOps to MLOps Bootcamp | Gourav J. Shah | 18k+ | ⭐ 4.6 | 12+ | DevOps to MLOps transition | $16.99 |
| 3 | Complete MLOps Bootcamp (10+ Projects) | Krish Naik | 31k+ | ⭐ 4.5 | 51+ | Maximum project variety | $17.99 |
| 4 | LLMOps Masterclass 2026 | Manifold AI Learning | 5k+ | ⭐ 4.5 | 16+ | LLM and GenAI operations | $14.99 |
| 5 | Full-Stack AI Engineer 2026 | School of AI | 4k+ | ⭐ 4.6 | 33+ | Beginner to production AI | $18.99 |
| 6 | AI Engineer MLOps Track | Ed Donner | 20k+ | ⭐ 4.8 | 18+ | Agentic AI deployment | $16.99 |
| 7 | MLflow in Action | J Garg | 8k+ | ⭐ 4.5 | 12+ | Deep MLflow mastery | $13.99 |
| 8 | Deployment of ML Models | Soledad Galli | 43k+ | ⭐ 4.5 | 10+ | Deployment fundamentals | $12.99 |
How to Choose the Right MLOps Course for You
You’re a complete beginner with limited ML background → Start with #5 Full-Stack AI Engineer to build your foundations, then follow up with #8 Deployment of ML Models to nail the production side.
You’re a DevOps engineer making the move into MLOps → #1 MLOps Zero to Hero or #2 Ultimate DevOps to MLOps Bootcamp are built exactly for you. Both leverage your existing Kubernetes and CI/CD knowledge.
You want the most project experience for your portfolio → #3 Complete MLOps Bootcamp gives you 10+ end-to-end projects across traditional ML, NLP, AWS, and GenAI. It’s the strongest portfolio builder on this list.
You’re focused on AWS and enterprise cloud deployments → #1 MLOps Zero to Hero for comprehensive AWS/Kubernetes coverage, or #6 AI Engineer MLOps Track for multi-cloud production deployments with Terraform and Bedrock.
Your work is shifting toward LLMs and Generative AI → #4 LLMOps Masterclass for LLM-specific operations, or #6 AI Engineer MLOps Track for a more complete GenAI deployment curriculum including AI agents.
You want to go deep on one specific tool → #7 MLflow in Action is the most targeted option. Twelve hours, one tool, real depth.
You’re a data scientist deploying your first production model → Start with #8 Deployment of ML Models. Soledad Galli’s approach to production code quality will save you from some genuinely painful early mistakes.
Not sure where to start? Browse all AI and ML courses by Topics or check the full Courses catalog on CoursesWyn to find the right fit for your learning path.
Frequently Asked Questions
Are these Udemy MLOps courses updated for 2026?
Yes. All eight courses were verified in February 2026 to confirm recent updates. They include current content on LLMOps, cloud-native MLOps with AWS SageMaker and Kubernetes, GenAI integration, agentic AI workflows, and modern monitoring tools like Prometheus and Grafana.
Do I need prior ML knowledge to take these courses?
It depends on the course. #8 Deployment of ML Models and #5 Full-Stack AI Engineer are both accessible with limited ML background. #1 MLOps Zero to Hero, #2 Ultimate DevOps to MLOps Bootcamp, and #6 AI Engineer MLOps Track work best if you already have basic ML or DevOps experience.
How much do these courses cost?
During Udemy sales — which happen frequently throughout the year — all eight courses are available between $12.99 and $18.99.
What MLOps tools do these courses cover?
Collectively, these eight courses cover the full modern MLOps stack: MLflow, DVC, Docker, Kubernetes, KServe, GitHub Actions, ArgoCD, Seldon Core, Prometheus, Grafana, AWS SageMaker, Amazon Bedrock, Apache Airflow, FastAPI, Terraform, and Hugging Face. Check the “What you’ll learn” sections above to match specific tools to your needs.
Will an MLOps course from Udemy help my career?
Absolutely — and the projects matter more than the certificate. A working CI/CD pipeline or a cloud-deployed ML model in your portfolio speaks far louder in technical interviews than any credential alone.
How long will it take to finish one of these courses?
The courses on this list range from 10 to 51 hours of video content. At a pace of 1–2 hours per day with dedicated project time, expect to complete a 12–18 hour course in about 3–6 weeks. Krish Naik’s 51-hour bootcamp is more realistically a 3–4 month journey at a sustainable pace.
Is MLOps a good career path in 2026?
Without question. MLOps engineers rank among the most in-demand tech roles in 2026, alongside AI/ML specialists and cloud engineers. The combination of ML knowledge with production engineering skills is genuinely rare — which is exactly why salaries remain strong and job listings consistently outpace available talent.
How does LLMOps differ from traditional MLOps in 2026?
LLMOps focuses on prompt drift, token usage, agent orchestration, and tools like LangSmith/Phoenix, while traditional MLOps handles model versioning & scaling. Courses #4 and #6 cover LLMOps specifically.
Conclusion
MLOps isn’t a trend that’s going to fade quietly. As more companies push AI out of notebooks and into production systems, the demand for engineers who can bridge that gap will only keep growing. The eight courses on this list represent the best of what Udemy has to offer right now — all verified and updated as of February 2026, all project-focused, all taught by instructors who’ve done this work for real.
Pick the one that matches where you are and where you’re heading. Work through the projects. Deploy something real. That’s how careers in this field actually get built.
All eight courses are available through the links above, typically for under $20 — grab the latest deals on our Udemy Coupon Code page.
Affiliate disclosure: CoursesWyn uses affiliate links. We earn a small commission at no extra cost to you when you purchase through our links. Our recommendations are based on genuine course evaluation — commissions never influence which courses we feature or how we rank them.
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