Definition
LLMOps is a systematic approach to developing, deploying, and operating Large Language Models (LLMs). By bringing consistency to this complex process, LLMOps helps ensure that organizations can derive the greatest possible value from LLM-based generative AI technology.
Large Language Models (LLMs) are a key technology for creating generative AI applications, which is software that uses AI to generate novel content. However, developing, deploying, and managing LLMs can be challenging due to the complexity of LLMs, as well as the vast quantities of data they interact with.
LLMOps helps to address these challenges by providing a consistent, predictable approach to working with LLMs.
LLMOps Overview
LLMOps – short for Large Language Model Operations – is the set of processes, practices, and tools that organizations use to deploy and manage LLMs.
LLMs are deep learning models that train on large volumes of data, and are the cornerstone of a number of the most innovative generative AI platforms and services that have emerged in recent years. These include the OpenAI platform, which uses the GPT family of LLMs, and the open source Llama LLMs developed by Meta.
Because LLMs are very complex, it can be challenging to deploy and operate them in a manner that meets the needs and priorities of a particular organization. For example, a business might need to control which types of data an LLM can access to mitigate potential data security risks. The business may also want to monitor the LLM’s behavior to identify issues such as slow responses to queries or a high incidence of hallucinations (meaning situations where the model generates inaccurate output).
LLMOps addresses requirements like these by offering a systematic and consistent approach to managing LLMs. Having an LLMOps strategy in place helps organizations take full advantage of LLMs while mitigating the operational challenges and risks that LLM technology poses.
Why Is LLMOps Important?
LLMOps is not strictly necessary for benefitting from LLMs or generative AI technology. However, because LLMOps helps to standardize and systematize the LLM development and management process, it positions organizations to leverage the greatest value from generative AI, while also helping to mitigate risks.
In this respect, LLMOps is similar to the Software Development Lifecycle (SDLC) – a set of practices that organizations typically use to develop, deploy, and manage software. Theoretically, it’s possible to deliver software haphazardly, without a set of coherent SDLC processes in place. However, doing so would likely lead to inefficient and risk-prone software development and deployment practices, which is why most teams standardize their software delivery operations based on the SDLC model. In a similar fashion, LLMOps is important as a way of bringing consistency, predictability, efficiency, and scalability to the process of using LLMs.
LLMOps is not necessary for organizations that use generative AI technology only via third-party solutions in which an external vendor assumes full responsibility for managing LLM development, deployment, and operations. However, businesses that build and/or operate LLMs themselves – including LLMs they develop from scratch, as well as third-party LLMs that they customize – can benefit from LLMOps to streamline the process.
Stages of LLMOps
LLMOps works by breaking the LLM development, deployment, and operations processes into a set of distinct stages, including:
- Data collection: Gathering large amounts of data from various sources, such as books, articles, and internet forums.
- Data preprocessing: This step involves cleaning the dataset by replacing missing values, removing duplicate information, and normalizing or scaling data to ensure uniformity.
- Model selection: The business decides which foundational model to use to support a particular use case. It may choose an existing model developed by a third party, or (if it has the requisite software development and ML resources and expertise among its staff) it might decide to create its own LLM.
- Model evaluation: By feeding engineered prompts into the model and analyzing the output, the business evaluates how well the model performs at the desired use case.
- Optimization and fine-tuning: To improve the model’s suitability to serve a target use case, engineers optimize it through techniques like fine-tuning (which customizes the behavior of a pre-trained model to respond better to a specific set of data).
- Deployment: Once optimization is complete, the organization can deploy the model, which makes it available for production use.
- Monitoring: Ongoing monitoring evaluates the model’s behavior to identify potential performance, accuracy, and security risks.
Components of LLMOps
Throughout each of these stages, LLMOps provides the following features and capabilities:
- Data management: LLMOps manages data at all stages of the model lifecycle – from preprocessing and training data through to data generated during inference – by helping to track and consolidate data resources with which models are able to interact.
- Security: To secure LLMs against abuse, LLMOps monitors for and helps mitigate risks like prompt injection attacks, which occur when bad actors insert malicious prompts into models. LLMOps can help manage this risk by assessing how users interact with models and detecting malicious prompts.
- Scalability: To ensure that models can handle increased workloads and user demands without compromising on performance, LLMOps helps keep model operations efficient and scalable. For instance, LLMOps can assist in detecting significant delays between when users enter a request and when the model responds, an issue that could reflect a lack of sufficient compute or memory resources within the infrastructure that hosts the model.
- Version control: LLMOps can provide version control features that help teams manage multiple versions of language models. Version control enables easy rollbacks to an earlier version of a model in the event that a more recent version exhibits bugs or security issues.
Differences Between LLMOps and MLOps
LLMOps is among the set of practices and processes known as Machine Learning Operations, or MLOps. The purpose of MLOps is to standardize and streamline the various workflows necessary to design, develop, train, deploy, and operate machine learning models of any kind.
Because LLMs are one type of machine learning model, they can be managed according to the principles of MLOps. However, MLOps also supports other types of models, not just LLMs. This is the key differentiator between MLOps in general and LLMOps.
In addition, LLMOps includes some special considerations that are not always relevant within other MLOps use cases, such as:
- Massive scalability: To perform well, LLMs often require especially rigorous scalability capabilities, due to the vast quantities of data necessary to train them and the large volume of queries they may receive during inference. For this reason, LLMOps may necessitate a higher degree of scalability than more traditional MLOps workflows.
- Tuning: Tuning tends to play a more important role in LLM development than it does in other MLOps contexts. This is mainly because in LLMOps, teams often start with a generic foundational model that they then tune and customize to fit their intended use case.
- Feedback loops: In many cases, a goal of LLMOps is to improve a model’s operations over time. This makes feedback loops – or the ability to collect feedback from model performance, and then use it to develop and deploy enhanced versions of the model – an especially important part of LLMOps.
- Security: Due to security challenges like potential prompt injection vulnerabilities, as well as the risk that the data that one organization shares with a third-party LLM could become visible to external organizations if it is not managed properly, LLMOps entails special security and data privacy protections. These may not be relevant in situations where an organization deploys an ML model only for internal use, with the result that there is no risk of leaking data to third parties.
LLMOps with JFrog ML
As an end-to-end AI model management platform, JFrog ML helps organizations streamline the processes of building, deploying, managing, and monitoring LLMs – and, for that matter, any type of ML model.
Continue to explore more about AI and ML using the links below, or see the platform in action by scheduling either a private demo or group demo, or starting a free trial at your convenience.