Large Language Model Operations: Understanding LLMOps

Large language model operations (LLMOps) describe the process of creating, deploying, and maintaining large language models (LLMs). As LLM technology develops and spreads to more companies and applications, researchers and developers are defining the best practices for working with LLMs, from streamlining deployment to optimizing response quality to implementing security. 

Learn more about large language model operations and implementing LLMOps at every stage of the LLM life cycle, as well as careers that rely on the LLMOps framework.

What is LLMOps?

LLMOps refers to the processes and best practices to build, train, deploy, maintain, and monitor a large language model. LLMOps provides you with the tools and resources to manage all of the aspects of developing an LLM in an efficient and scalable way that helps you resolve bottlenecks and create a model that performs better. LLMOps also addresses areas like compliance and security to help you reduce problems in your development process and in LLM management. 

LLMOps is a category within machine learning operations. As LLMs operate using machine learning and artificial intelligence principles, the procedures for managing them look similar. LLMOps is specialized for large language models and goes deeper than machine learning operations.

Stage of LLMOps: LLM life cycle 

You can implement LLMOps at every stage of your LLM development life cycle. While your project may vary from the average, developing a large language model includes creating, training, testing, deploying, and maintaining your LLM. Explore LLMOps best practices at every stage: 

Exploratory data analysis

The first stage of the LLMOps life cycle is exploratory data analysis. In this step, you’ll begin creating data sets by collecting and cleaning data. This data will eventually become what you use to train your model. You will collect data from a variety of sources to gain a robust understanding of its characteristics. You will create tables, data visualizations, and other resources in this step. 

Data preprocessing and prompt engineering

Next, you’ll continue to prepare the data for training your LLM and begin to write the prompts you’ll use to generate the appropriate response. You may need to label and annotate the data to provide context for how your LLM should make decisions; you might also want to organize and store the data so you can easily retrieve information as you or your team members need it. Writing prompts is an important step because the instructions you give your LLM will play a big part in determining the quality of your response. 

Training and fine-tuning

You will use machine learning algorithms to help your model understand and identify the patterns in your training data. You will assess the model’s performance before fine-tuning it to optimize results. Evaluating model performance involves tracking errors, reliability, and bias and studying how well your model performs different tasks. You can use open-source libraries like TensorFlow and Hugging Face Transformers to help you adjust the parameters of your LLM to influence performance. You can also fine-tune your model to be an expert in certain topics or to perform certain tasks. 

Model governance 

Model governance is a process of tracking your model’s versions during development, which can help you collaborate with your team or others using an MLOps platform. As a result, you may review your model’s safety and reliability and hunt for bias or weaknesses in security. 

Monitoring and deployment

This stage of development is where you deploy your LLM. You will need to develop a process for updating your model as well, using strategies like online or batch inference models to push out updates and infrastructure as needed. You will develop operational metrics like system health and use statistics to help you monitor your LLM. Strategies like continuous integration and deployment can help you manage your pipelines for all versions of your model. Including real-world user feedback can help you maintain your continuous integration/continuous delivery (CI/CD) workflow by integrating feedback into subsequent updates. 

Developing APIs

If you want to integrate your LLM into other applications, you will need to develop an application programming interface (API) and an API gateway. An API allows your LLM to communicate with other software to integrate, and an API gateway helps you manage multiple API requests by offering tools like authentication and load distribution. If you offer APIs, you will also need to monitor API performance to ensure that everyone can use your LLM optimally. 

Security and compliance

Security and compliance are stages of the LLM operations process that you will want to return to continually to ensure that your product is safe and complies with regulations. You can use frameworks like the AI Risk Management Framework to help you identify potential security concerns and structure your security operations. Developed by the National Institute of Standards and Technology, this list of best practices is one example of LLMOps you can adopt. 

Who’s on an LLMOps team?

An LLMOps team might include professionals like data scientists, machine learning or LLM engineers, and data engineers. Explore the day-to-day responsibilities for each of these roles as well as the average salary and job outlook you can expect in the field. 

Data scientist

Average annual salary in the US (Glassdoor): $118,281 [1]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As a data scientist, you will work with a company or organization to analyze data and unlock insights that your leadership can use to make intelligent decisions. You will determine what data you need, collect and process the data, analyze the data, and present your findings to senior leadership. In this role, you will work with large language models to optimize many processes within data science. 

LLM engineer

Average annual salary in the US (Glassdoor): $124,773 [3]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As an LLM engineer, you will be a machine learning engineer specializing in large language models. You will use LLMOps to help determine your workflow and the processes involved in each stage of the LLM development cycle. In this role, you will develop and train LLMs for various uses. 

Data engineers

Average annual salary in the US (Glassdoor): $106,593 [4]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As a data engineer, you will focus on the stage of the LLMOps pipeline, where you build systems that store and aggregate data in an accessible way, allowing all company members to access the data they need. You will be in charge of creating and designing the data pipeline that moves raw data to data sets that are easy to use and reliable. 

Learn more about LLMOps on Coursera

LLMOps is the set of best practices and industry standards that LLM developers follow to provide structure to each stage of the LLM development cycle. If you want to learn more about LLMOps, explore Professional Certificates to help you build job-ready skills for a role working with LLMOps, like the IBM Machine Learning Professional Certificate, where you’ll master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles.