> ## Content Index > Fetch the complete content index at: https://skillslane.com/llms.txt > Use this file to discover other available public pages before exploring further. # 7 Best LLMOps Courses to Learn in 2026 - URL: https://skillslane.com/best-llmops-courses/ - Published: 2026-10-20T05:13:38.000Z - Updated: 2026-10-20T05:13:38.000Z - Author: Ann Mary Joy - Tags: LLMOps, courses, upskill If you're new to [LLMOps](https://skillslane.com/llmops-aiops-analysis/), you'll probably want a course that takes you through the basics before getting into the more technical aspects. I've shortlisted seven LLMOps courses worth exploring. They range from short introductions to longer programs, so there's a mix of options depending on how much time you want to spend learning. ## List of Top LLMOps Courses If you don't have time to read this blog fully, here is a quick overview of LLMOps courses. | Course | Best For | Provider | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------- | --------------- | | [Large Language Model Operations (LLMOps) Specialization](https://www.coursera.org/specializations/large-language-model-operations?utm%5Fsource=chatgpt.com) | End-to-end LLMOps | Coursera | | [LLMOps Concepts](https://www.datacamp.com/courses/llmops-concepts?utm%5Fsource=chatgpt.com) | LLMOps fundamentals | DataCamp | | [Building Real-World Applications With Large Language Models](https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455?utm%5Fsource=chatgpt.com) | Real-world LLM applications | Udacity | | [LLMOps](https://www.educative.io/courses/llmops?utm%5Fsource=chatgpt.com) | Production LLM systems | Educative | | [LLMOps and AIOps Bootcamp](https://www.udemy.com/course/llmops-and-aiops-bootcamp-with-9-end-to-end-projects/?utm%5Fsource=chatgpt.com) | Hands-on LLMOps projects | Udemy | | [Large Language Model Operations (LLMOps) Professional Certificate](https://www.edx.org/certificates/professional-certificate/ai-large-language-model-operations-llmops?utm%5Fsource=chatgpt.com#courses) | Enterprise LLM deployment | edX | | [LLMOps](https://www.deeplearning.ai/courses/llmops?utm%5Fsource=chatgpt.com) | LLM pipelines and fine-tuning | DeepLearning.AI | Now let's go over each course in detail. ## 1\. Large Language Model Operations (LLMOps) Specialization by Duke University on Coursera ![Large Language Model Operations (LLMOps) Specialization by Duke University on Coursera](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-45.png) If you're looking to learn LLMOps in more depth, this [**LLMOps specialization from Duke University on Coursera**](https://www.coursera.org/specializations/large-language-model-operations?ref=skillslane.com) is worth exploring. It covers several aspects of working with large language models. Just keep in mind that there's quite a bit to get through here. Coursera has put together six courses in this specialization, and it takes roughly five months to complete if you study ten hours a week. You won't be spending all your time learning about one particular tool here. The specialization takes you through a few different areas, starting with generative AI and moving on to cloud deployment, data engineering, and open-source models. Here's a closer look at the six courses included in the specialization. ### Course 1: Introduction to Generative AI This is where the specialization begins. The course introduces **generative AI** and explores how it can be used to develop software solutions and automate tasks. You'll also learn about prompt engineering and how prompts can be designed to get more useful outputs from generative AI tools. It's a starting point for understanding the technology before moving into the more operational parts of the program. #### What You'll Learn - You'll explore how [generative AI](https://skillslane.com/best-generative-ai-courses/) can be used to automate tasks and develop software solutions. - You'll learn how [prompt engineering](https://skillslane.com/best-prompt-engineering-courses/) can influence the responses generated by an LLM. - You'll get an introduction to generative AI applications and related concepts. ### Course 2: Operationalizing LLMs on Azure Once you have an introduction to generative AI, the next course looks at working with large language models on Microsoft Azure. The focus here is on deploying and managing LLM applications in a cloud environment. You'll also explore Semantic Kernel, a framework used to build applications powered by language models, and learn how retrieval-augmented generation (RAG) can help applications work with relevant external information. #### What You'll Learn - You'll learn how to deploy and manage large language models using Azure. - You'll explore Semantic Kernel and how it can be used to build LLM-powered applications. - You'll learn about implementing RAG patterns in applications. ### Course 3: Advanced Data Engineering Data engineering might not be the first thing that comes to mind when you think about LLMOps, but it plays an important part in managing the information that applications rely on. This course focuses on data pipelines, message queues, and databases. You'll explore ways to move and organize data and work with different database types, including vector databases, graph databases, and key-value databases. #### What You'll Learn - You'll get to see how data pipelines are built and managed, and why they matter when working with large amounts of data. - You'll learn what message queues do and how they help data move between different parts of a system. - You'll look at different kinds of databases and learn where each one fits when handling data at scale. ### Course 4: GenAI and LLMs on AWS In this course, you'll work with [AWS](https://skillslane.com/top-16-aws-certification-courses-for-free-and-paid/) and learn how its cloud services come into play when building generative AI applications. It also covers the basics of AWS cloud computing, so you can understand how the platform supports this kind of work. One of the technologies covered is Amazon Bedrock, which provides access to foundation models for building generative AI applications. #### What You'll Learn - You'll learn the basics of AWS cloud computing and how it relates to machine learning. - You'll explore how AWS services, including Amazon Bedrock, can be used to build generative AI solutions. - You'll learn about developing and deploying machine learning applications using AWS. ### Course 5: Databricks to Local LLMs Not every LLM application needs to rely entirely on a cloud-hosted model. Depending on the use case, developers may also explore running models locally. This course looks at working with Databricks for data engineering and machine learning tasks before moving into local LLMs. It introduces tools such as Llamafile and explores local models, including Mixtral. #### What You'll Learn - You'll learn how Databricks can be used for data engineering and machine learning workloads. - You'll explore how to create and manage machine learning pipelines. - You'll get an introduction to running local LLMs using tools such as Llamafile. ### Course 6: Open Source LLMOps Solutions The final course focuses on open-source LLMs and the work involved in running and fine-tuning them. You'll explore how to run large language models locally and learn about fine-tuning models for different needs. The course also covers **open-source generative AI tools**, giving you an opportunity to explore alternatives to relying solely on hosted models. #### What You'll Learn - You'll learn how to run large language models locally. - You'll explore fine-tuning and how it can be used to adapt models. - You'll work with open-source generative AI technologies and related tools. ### Course Details The table below covers some details you might want to know. | **Detail** | **Information** | | ----------- | ------------------------------------ | | Level | Beginner | | Duration | Approx 5 months at 10 hours per week | | Cost | Free, paid options are available | | Format | Online lessons and hands-on projects | | Certificate | Shareable specialization certificate | > **Best suited for:** Learners who want to explore LLMOps across generative AI, data engineering, cloud deployment, and open-source models through a more extensive learning program. ## 2\. LLMOps Concepts by DataCamp ![LLMOps Concepts by DataCamp](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-46.png) You may already know how large language models work, but have you ever wondered what it takes to keep an application running smoothly once it's built? That's what you'll get into with [**DataCamp's LLMOps Concepts**](https://www.datacamp.com/courses/llmops-concepts?ref=skillslane.com) course. It looks at the work that goes into managing LLM applications, beyond just building them. The course takes you through the LLM application lifecycle, starting with the initial idea and moving towards development and deployment. It also covers [prompt engineering](https://skillslane.com/best-prompt-engineering-certifications/), agents, chains, RAG, fine-tuning, testing, and monitoring. I found the operational side particularly interesting. It's easy to focus on getting an LLM to produce a useful response, but there's more to managing an application than that. You also need ways to evaluate its performance, keep track of costs, and deal with security concerns. The course takes about an hour, so it isn't intended to cover every topic in great depth. Still, it offers a way to familiarise yourself with the terminology and decisions involved in LLMOps before moving on to more technical training. 👉 One thing to check before enrolling is the prerequisite. DataCamp lists its ****Large Language Models (LLMs) Concepts** course as recommended preparation. ### What You'll Learn - You'll learn what LLMOps means and how it differs from traditional [MLOps](https://skillslane.com/mlops-job-trends/). - You'll explore the stages involved in developing and deploying an LLM application. - You'll understand the differences between approaches such as retrieval-augmented generation (RAG) and fine-tuning. - You'll learn about prompt engineering, testing, deployment, and monitoring. - You'll explore topics such as cost management, data governance, and application security. ### Course Details In the table below, I've listed a few details to help you decide whether this course suits your needs. | **Detail** | **Information** | | ----------- | ---------------------------------------- | | Level | Basic | | Duration | Approximately 1 hour | | Cost | Free | | Format | Online lessons and interactive exercises | | Certificate | Statement of accomplishment available | > **Best suited for:** Learners who want a short introduction to LLMOps concepts before getting into the technical implementation. ## 3\. LLMOps: Building Real-World Applications With Large Language Models by Udacity ![LLMOps: Building Real-World Applications With Large Language Models by Udacity](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-47.png) Some people prefer learning a concept and then immediately trying it out. If that sounds like you, [Udacity's LLMOps: Building Real-World Applications With Large Language Models](https://www.udacity.com/course/building-real-world-applications-with-large-language-models--cd13455?ref=skillslane.com) might be worth considering. The course moves beyond the basic terminology and looks at what goes into building and operating LLM applications. It covers fine-tuning, experiment tracking, model evaluation, prompt engineering, deployment, and monitoring. The project examples make the syllabus more concrete. According to Udacity, learners explore applications such as a customer support chatbot, an LLM-based evaluation system, and a clickbait detector. I like that these examples give the technical topics some context. Experiment tracking, for instance, becomes easier to understand when you can see why you might want to compare different model configurations or keep track of changes during development. [Udacity](https://skillslane.com/artificial-intelligence-online-courses-from-udacity/) recommends having some machine learning knowledge and being comfortable with Python before you start. It's listed as an intermediate course, so if you're still learning to code, I'd suggest getting comfortable with the basics first. You'll have a much easier time following along. ### What You'll Learn - You'll learn about the LLMOps lifecycle and how it relates to traditional [MLOps](https://skillslane.com/best-mlops-courses/). - You'll explore fine-tuning, prompt engineering, and ways to evaluate LLM outputs. - You'll work with concepts such as experiment tracking, model versioning, and debugging. - You'll learn about deploying, monitoring, and maintaining LLM applications. - You'll explore AI safety, privacy, and security considerations through practical examples. ### Course Details Here's a little more information to help you decide if this course is worth exploring. | **Detail** | **Information** | | ---------------- | ---------------------------------------------------- | | Difficulty Level | Intermediate | | Duration | Approximately 11 hours | | Cost | Free | | Format | Online lessons and practical projects | | Certificate | Check the current course page for credential details | > **Best suited for:** Learners with some Python and machine learning knowledge who want to practice building and managing real-world LLM applications. ## 4\. LLMOps: Building Production-Ready LLM Systems by Educative ![LLMOps: Building Production-Ready LLM Systems by Educative](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-48.png) An LLM application can work well during development and still run into problems once it's used in a real environment. [Educative's LLMOps: Building Production-Ready LLM Systems](https://www.educative.io/courses/llmops?ref=skillslane.com) looks at some of the work involved in making these applications more reliable. You'll get into quite a few things in this course, including RAG, embeddings, vector databases, data processing, and prompt versioning. It also covers how to deploy an LLM application, keep it secure, and deal with practical issues like running costs and slow responses. I would pay particular attention to the part about evaluating LLMs. Just because an application runs without errors doesn't necessarily mean it's doing a good job. Think about a chatbot that gives an answer every time you ask a question, but the answer isn't actually helpful. You need ways to check whether the model is giving relevant, reliable responses and doing what you built it to do. That's an important part of working with LLMs in practice. ### What You'll Learn - You'll learn how LLMOps differs from traditional machine learning operations. - You'll explore how RAG applications use document processing, embeddings, and vector databases. - You'll learn about managing prompts and testing LLM responses. - You'll explore evaluation methods, including the use of LLMs to assess other LLM outputs. - You'll examine security controls, deployment, and ways to manage cost and response time. ### Course Details Before moving on, here's a quick overview of what the course offers. | **Detail** | **Information** | | ---------------- | ------------------------------------------------------------------ | | Difficulty Level | Check the current course page for the recommended experience level | | Duration | Approximately 3 hours | | Cost | Check Educative for current access and subscription options | | Format | Interactive online lessons and project-based learning | | Certificate | Yes | > **Best suited for:** Learners who want to explore RAG, evaluation, security, and other practical considerations involved in running LLM applications. 💰 You can use [Educative Discount](https://skillslane.com/deals/educative-io-discount/) to get a massive discount. ## 5\. LLMOps and AIOps Bootcamp by Udemy ![LLMOps and AIOps Bootcamp by Udemy](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-49.png) If you'd rather spend time building projects than working through concepts alone, [**Udemy's LLMOps and AIOps Bootcamp**](https://www.udemy.com/course/llmops-and-aiops-bootcamp-with-9-end-to-end-projects/?couponCode=MT261012G1&ref=skillslane.com) is another option to look at. This course gets into the tools you'll need to build and run AI applications, including LangChain, vector databases, Docker, [Kubernetes](https://skillslane.com/learn-kubernetes-from-these-best-online-courses/), and cloud platforms like AWS and Google Cloud. You'll also come across CI/CD pipelines and monitoring tools such as **Prometheus** and **Grafana**. What I like about this course is that it doesn't stop at working with the LLM itself. It also looks at what goes on around it, from getting the application up and running to keeping an eye on how it's performing. If you're curious about the infrastructure side of LLMOps, there's quite a bit here to explore. You'll need some experience with [**Python**](https://skillslane.com/python-exception-handling-beginner-tutorial/)before taking this course, along with a basic idea of how generative AI works. If you're still getting comfortable with either of these, you may want to spend some time on them first, so you don't end up struggling to keep up with the lessons. ### What You'll Learn - You'll learn how to build and deploy AI applications using tools such as LangChain and vector databases. - You'll explore CI/CD pipelines and how they can automate parts of the development and deployment process. - You'll work with Docker and [Kubernetes](https://skillslane.com/kubernetes-certifications-report/) and explore deployment across AWS and Google Cloud. - You'll learn about monitoring and securing AI applications using tools such as Prometheus and Grafana. - You'll work through projects that bring together application development, deployment, and monitoring. ### Course Details If you're considering this course, here are a few things worth knowing. | **Detail** | **Information** | | ---------------- | ------------------------------------------------------------------- | | Difficulty Level | Technical; prior Python and generative AI knowledge recommended | | Duration | Approximately 30 hours | | Cost | Paid course; check Udemy for the current price and available offers | | Format | On-demand video lessons, demonstrations, and projects | | Certificate | Yes | > **Best suited for:** Developers who already have some Python and generative AI experience and want to practice deploying and monitoring AI applications. 💰 Check our [Udemy coupon](https://skillslane.com/deals/udemy-coupon/) to get a active discount for subscription, or course. ## 6\. Large Language Model Operations (LLMOps) by Pragmatic AI Labs on edX ![Large Language Model Operations (LLMOps) Professional Certificate by Pragmatic AI Labs on edX](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-50.png) If you're looking for something that covers more than just the basics of LLMOps, this [**program on edX**](https://www.edx.org/certificates/professional-certificate/ai-large-language-model-operations-llmops?ref=skillslane.com#courses) is worth a look. Offered by Pragmatic AI Labs, the Professional Certificate brings together seven courses covering [generative AI](https://skillslane.com/generative-ai-statistics/), cloud platforms, data engineering, and open-source language models. 💡 ****Before you enroll:** This is a longer program, so you'll need to set aside some time for it. edX estimates that it takes around seven months to complete, with three to six hours of study each week. You can work through the material at your own pace, which may help if you're balancing your learning with a job or other commitments. Here's a closer look at the seven courses included in the program. ### 1\. Introduction to Generative AI Before getting into the operational side of large language models, it helps to understand the technology you're working with. This course introduces generative AI, its capabilities, and the role prompts play in getting useful responses from AI models. #### What You'll Learn - You'll learn about generative AI models and the kinds of tasks they can perform. - You'll explore how prompts influence the responses these models produce. - You'll get a better understanding of how to write prompts that produce more useful outputs. ### 2\. Large Language Models with Azure This course focuses on working with large language models in Microsoft's Azure ecosystem. It covers Azure AI services and introduces the tools used to deploy and manage LLMs, including Azure Machine Learning and Azure OpenAI Service. #### What You'll Learn - You'll explore how Azure AI services can be used when working with large language models. - You'll learn about deploying LLMs and managing the resources they need. - You'll look at Azure Machine Learning and Azure OpenAI Service and how they fit into LLM workflows. - You'll also learn about GPU quotas, which matter when you're managing the computing resources needed to run models. ### 3\. Generative AI and LLMs on AWS If you're more interested in Amazon Web Services, this course takes you through the AWS side of working with generative AI. It covers deploying and managing LLMs, along with some of the practical concerns that come with running them in a cloud environment. #### What You'll Learn - You'll learn how AWS services can be used to deploy and manage large language models. - You'll explore ways to keep an eye on performance and manage the cost of running LLM applications. - You'll look at monitoring metrics to understand how your applications are performing. - You'll also explore secure pipelines and the importance of meeting relevant regulatory requirements. ### 4\. Data Engineering with Databricks Data engineering is another part of the LLMOps picture, and this course introduces Databricks in that context. The focus is on understanding how data engineering tools fit into workflows that support AI and machine learning applications. #### What You'll Learn - You'll explore the role of Databricks in data engineering workflows. - You'll learn why data management and processing matter when building applications that rely on large language models. - You'll get an introduction to the data engineering side of AI workflows and how it supports the wider LLMOps process. ### 5\. Open Source LLMOps Not every LLM application needs to rely entirely on proprietary models. This course looks at open-source options, including models such as LLAMA and Mistral, and explores how they can be adapted and deployed. #### What You'll Learn - You'll explore open-source large language models and their use in AI applications. - You'll learn about fine-tuning models using custom datasets. - You'll look at containerization and how it can help with model deployment. - You'll also get a better understanding of the practical considerations involved in working with open-source LLMs. ### 6\. Advanced Data Engineering This course goes further into the data engineering side of things. The program highlights tools and technologies such as Celery, RabbitMQ, Airflow, and graph databases, which are used in different parts of data processing and workflow management. #### What You'll Learn - You'll explore tools used to coordinate and manage data processing tasks. - You'll learn about message queues such as RabbitMQ and task-processing tools such as Celery. - You'll look at workflow orchestration with Airflow. - You'll also explore graph databases and their role in handling connected data. ### 7\. Applied Local Large Language Models This course focuses on local large language models, giving you an opportunity to explore this area alongside the program's cloud-based material. Local models can be useful in situations where developers want more control over how models are run and integrated into their applications. #### What You'll Learn - You'll explore the use of large language models that can run in local environments. - You'll learn about working with models outside a fully cloud-hosted setup. - You'll look at how local models fit into the wider process of building and running LLM applications. #### Course Details I've put the essential information about this course together below for easy reference. | **Detail** | **Information** | | ---------------- | ----------------------------------------------- | | Difficulty Level | Advanced | | Duration | Approximately 7 months at 3 to 6 hours per week | | Cost | $1,568.70 | | Format | Self-paced online learning across seven courses | | Certificate | Yes | > **Best suited for:** Learners who want to explore LLMOps across cloud platforms, open-source models, and data engineering rather than concentrate on just one part of the workflow. ## 7\. LLMOps by DeepLearning.AI ![LLMOps by DeepLearning.AI](https://storage.ghost.io/c/74/7e/747e7ce7-cfcb-4f5a-b05a-d92d755328e1/content/images/2026/10/image-51.png) Not every LLMOps course needs to run for several weeks. [**DeepLearning.AI's LLMOps course**](https://www.deeplearning.ai/courses/llmops?ref=skillslane.com) is a much shorter option, with approximately one hour and twenty-one minutes of content. The course focuses on the operational pipeline for fine-tuning and deploying an LLM. It covers preparing training data, supervised fine-tuning, versioning data and models, and using pipelines to automate parts of the process. It also introduces safety scoring, which can help monitor and filter harmful content in an application. This is an important part of the subject because deploying an LLM involves more than getting it to generate an answer. You also need to consider how its outputs will be handled and evaluated. The course includes code examples and uses tools such as **BigQuery, Kubeflow Pipelines**, and **Google Cloud.** The page also lists an accomplishment option through its PRO offering, so check the current terms if you want a credential. ### What You'll Learn - You'll learn how to prepare training data for supervised fine-tuning. - You'll explore how to version datasets and tuned models to keep track of experiments. - You'll work with an open-source pipeline for training and deploying a customized LLM. - You'll learn how pipeline automation can support an LLMOps workflow. - You'll explore safety scoring and ways to monitor an application's outputs. ### Course Details Here's a quick overview to help you get a better sense of what signing up for this course involves. | **Detail** | **Information** | | ---------------- | ----------------------------------- | | Difficulty Level | Beginner | | Duration | Approximately 1 hour and 21 minutes | | Cost | Free access for a limited time | | Format | Online video lessons | | Certificate | Yes | > **Best suited for:** Learners who want a short, focused introduction to fine-tuning pipelines, model versioning, and deployment. ## Conclusion Honestly, I think it helps to be a little realistic about what you want to learn from an LLMOps course. You might come across a program that covers everything from RAG to cloud deployment, but that doesn't necessarily mean it's the right fit for you. If you're still trying to understand how LLM applications work, a course that takes things slowly might suit you better. And if you've already worked with LLMs, you may want something that lets you try out the more technical side. Say you've built an application using an LLM, but you're not quite sure how to tell if it's giving good answers. You could start by learning about evaluation. If getting the application deployed is what's holding you back, that's probably a better place to begin. You don't have to follow someone else's learning path just because that's how a course is structured. I'd look at the courses, see which one covers the things I'm interested in, and take it from there. You may find that one course gives you enough to get started, or you may need a couple of them to get a clearer picture of LLMOps. Either way, you can figure out the next step as you go. There's no need to have the whole thing mapped out before you've even begun.