What is LLMOps?

Large language models (LLMs) are machine learning (ML) models that perform language-related tasks such as translation, answering questions, chat and content summarization, as well as content and code generation. LLMs like GPT-3, LLaMA, and Falcon are innovative tools that can be trained on datasets and answer questions. As these tools continue to evolve, organizations need best practices on how to operationalize these models. That’s where LLMOps comes in.

Large Language Model Operations (LLMOps) are the practices, techniques and tools used for the operational management of large language models in production environments. LLMOps is specifically for managing and automating the lifecycle of LLMs from fine-tuning to maintenance using tools and methodologies. With model specific operations, data scientists, engineers, and IT teams can efficiently deploy, monitor, and maintain large language models.

If LLMs are a subset of ML models, then LLMOps is a large language model equivalent to machine learning operations (MLOps). MLOps is a set of workflow practices aiming to streamline the process of deploying and maintaining ML models. MLOps seeks to establish a continuous evolution for integrating ML models into software development processes. Similarly, LLMOps seeks to continuously experiment, iterate, deploy and improve the LLM development and deployment lifecycle.

While LLMOps and MLOps have similarities, there are also differences. A few include:

Learning: Many traditional ML models are created or trained from scratch, but LLMs start from a foundation model and are fine-tuned with new data to improve performance in a specific domain.

Tuning: For LLMs, fine-tuning improves performance for specific applications and enhances accuracy, bringing more knowledge about a specific subject. Prompt tuning is an efficient and streamlined process that enables LLMs to perform better on specific tasks. Hyperparameter tuning is also a difference. In traditional ML, tuning focuses on improving accuracy or other metrics. Whereas for LLMs, tuning is also important for reducing the cost and computational power requirements of training and inference. Both classical ML models and LLMs benefit from tracking and optimizing the tuning process, but with different emphases. Lastly, it's important to mention an additional process that can be used alongside tuning to improve the accuracy of the answers, which is retrieval-augmented generation (RAG). RAG is the process of using an external knowledge base to ensure current and accurate facts are provided to the LLM when querying it so that it can generate a better response.

Feedback: Reinforcement learning from human feedback (RLHF) is an improvement in training LLMs. Because tasks are often very open ended, human feedback from your application’s end users is important for evaluating LLM performance. LLMs use human feedback to evaluate prompt responses for accuracy and coherency, whereas traditional ML models use specific and automated metrics for accuracy.

Performance metrics: ML models have clearly defined performance metrics, such as accuracy, AUC, F1 score, etc. LLMs, however, have a different set of standard metrics and scoring apply — such as bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE).

With LLMOps becoming the optimal way to monitor and enhance the performance of LLMs over time, there are  three primary benefits to discuss:

Efficiency: LLMOps allows teams to achieve faster model and pipeline development, deliver higher-quality models, and deploy to production faster. Streamlined efforts can help teams collaborate better on a unified platform for communication and insights sharing.

Scalability: LLMOps enables greater scalability and management where multiple models can be overseen, controlled, managed, and monitored for continuous integration and continuous delivery/deployment (CI/CD). LLM pipelines can encourage collaboration, reduce conflicts and speed release cycles, and by improving model latency, LLMOps provides a more responsive user experience.

Risk reduction: LLMOps enables greater transparency and faster response to requests and ensures greater compliance with an organization’s or industry’s policies. Advanced LLMOps can improve security and privacy by prioritizing the protection of sensitive information, helping prevent vulnerabilities and unauthorized access.

There are a few use cases for LLMOps.

Continuous integration and delivery (CI/CD): CI/CD aims to streamline, accelerate, and automate the model development lifecycle. Tools like Jenkins can be used to build and test projects continuously, making it easier for developers to integrate changes to the project, and making it easier for users to obtain a fresh build. This enables seamless model updates and rollbacks, minimizing disruption to users.

Data collection, labeling, storage: Data collection can draw from a sufficient variety of sources, domains and languages. Data labeling with human input can provide complex, domain-specific judgment. Data storage with suitable database and storage solutions can collect and retain digital information throughout the LLM lifecycle.

Model fine-tuning, inference, monitoring: Model fine-tuning optimizes models to perform domain specific tasks. Model inference can manage production based on existing knowledge and perform the actions based on the inferred information. Model monitoring, including human feedback, collects and stores data about the model behavior to learn how models behave with real production data.

There are several stages or components of LLMOps and best practices for each:

Exploratory data analysis (EDA): The process of investigating data to discover, share, and prepare for the machine learning lifecycle by creating data sets, tables, and visualizations.

• Data collection: The first step that will be used to train the LLM collected from a variety of sources, such as code repositories and social media.
• Data cleaning: Once collected, data needs to be cleaned and prepared for training, which includes removing errors, correcting inconsistencies, and removing duplicate data.
• Data exploration: The next step is to explore the data to better understand its characteristics, including identifying outliers and finding patterns.

Data prep and prompt engineering: The process of making data visible and shareable across teams and developing prompts for structured, reliable queries to LLMs.

• Data preparation: The data used to train an LLM is prepared in specific ways, including the removal of stop words and normalizing the text.
• Prompt engineering: The creation of prompts that are used to generate text that helps ensure that LLMs generate the desired output.

Model fine-tuning: The use of popular open source libraries such as PyTorch to fine-tune and improve model performance.

• Model training: After the data is prepared, the LLM is trained, or fine-tuned, by using a machine learning algorithm to learn the patterns in the data.
• Model evaluation: Once trained, the LLM needs to be evaluated to see how well it performs, by using a test set of data that was not used to train the LLM.
• Model fine-tuning: If the LLM doesn’t perform well, it can be fine-tuned, which involves modifying the LLM’s parameters to improve its performance.

Model review and governance: The process of discovering, sharing and collaborating across ML models with the help of an open source MLOps platforms such as MLflow and Kubeflow.

• Model review: Once fine-tuned, the LLM needs to be reviewed to ensure that it is safe and reliable, which includes checking for bias, safety, and security risks.
• Model governance: Model governance is the process of managing the LLM throughout its lifecycle, which includes tracking its performance, making changes to it as needed, and retiring it when it is no longer needed.

Model inference and serving: The process of managing the frequency of model refresh, inference request times, and similar production specifics in testing.

• Model serving: Once the LLM is reviewed and approved, it can be deployed into production, making it available through an application programming interface (API).
• Model inference: The API can be queried by an application to generate text or answer questions. This can be done through a variety of ways, such as a representational state transfer application programming interface (REST API) or a web application.

Model monitoring with human feedback: The creation of model and data monitoring pipelines with alerts both for model drift and for malicious user behavior.

• Model monitoring: Once deployed, the LLM needs to be monitored to ensure that it is performing as expected, which includes tracking its performance, identifying any problems, and making changes as needed.
• Human feedback: This is used to improve the performance of the LLM, and it can be done by providing feedback on the text that the LLM generates, or by identifying any problems with the LLM’s performance.

An LLMOps platform provides data scientists and software engineers with a collaborative environment that facilitates data exploration, coworking capabilities for experiment tracking, prompt engineering, and model and pipeline management. It also provides controlled model transitioning, deployment, and monitoring for LLMs.

The platform can deliver more efficient library management, which lowers operational costs and enables less technical personnel to complete tasks. These operations include data preprocessing, language model training, monitoring, fine-tuning and deployment. LLMOps automates the operational, synchronization and monitoring aspects of the machine learning lifecycle.

As the industry's leading hybrid cloud application platform powered by Kubernetes, Red Hat® OpenShift® accelerates the rollout of AI-enabled applications across hybrid cloud environments, from the datacenter to the network edge to multiple clouds.

With Red Hat OpenShift, organizations can automate and simplify the iterative process of integrating models into software development processes, production rollout, monitoring, retraining, and redeployment for continued prediction accuracy.

Red Hat OpenShift AI  is a flexible, scalable MLOps platform with tools to build, deploy, and manage AI-enabled applications. It allows data scientists and application developers to simplify the integration of artificial intelligence (AI) into applications securely, consistently and at scale. OpenShift AI provides tooling that supports the full lifecycle of AI/ML experiments and models, on-premise and in the public cloud.

By combining the capabilities of Red Hat OpenShift AI and Red Hat OpenShift into  a single enterprise-ready AI application platform, teams can work together in a single collaborative environment that promotes consistency, security, and scalability.