LLMOps refers to the set of practices and tools used to manage, streamline, and operationalize large language models.
LLMOps is a portmanteau of ‘LLM’ and ‘MLOps’.
LLMs (Large language models) are a type of foundation model that can perform a variety of NLP tasks, including generating and classifying texts, answering questions in a conversational manner and translating texts.
MLOps (Machine Learning Operations) is a discipline that streamlines and automates the lifecycle of ML models.
LLMOps applies MLOps principles and the MLOps infrastructure to LLMs. For this reason, it is considered a subset of MLOps.
Why Do We Need LLMOps?
Large language models, like GPT-3.5, are highly complex and resource-intensive. They require specialized techniques and infrastructure for their development, deployment, and maintenance.
Here are some of the challenges of operationalizing LLMs:
Model Size and Complexity – LLMs are very large and complex models. This makes them difficult to train, fine-tune, and deploy.
Data Requirements – LLMs require massive datasets of text and code to train. This can be a challenge to collect and curate.
Infrastructure Requirements – LLMs require a lot of computational power and storage. This can be a challenge to provision and manage.
Performance – Ensuring LLM performance at scale requires computational resources, time, and highly skilled professionals, which are not always available.
Security and Privacy – LLMs can be used to generate sensitive text, such as personal information or creative content. It is important to implement security and privacy measures to protect this data.
Interpretability – LLMs are often opaque and difficult to interpret. This can make it challenging to understand how they make decisions and to ensure that they are not biased.
Ethical Considerations – LLMs may be subject to bias, toxicity, hallucinations, or other ethical concerns. It is important to implement guardrails to protect against these risks.
LLMOps aims to address the unique challenges associated with managing LLMs and ensure their efficient and effective operation in production environments. LLMOps helps deploy applications with LLM models securely, efficiently, and at scale.
What Does LLMOps Include?
Some of the key aspects of LLMOps are:
Data Creation, Curation and Management – Organizing, storing, and preprocessing the large amounts of data required for training language models. This includes data versioning, ingestions, and data quality checks.
Model Training – Implementing scalable and distributed training processes to train large language models. Includes techniques like parallel processing, distributed computing, and automated hyperparameter tuning.
Model Deployment – Deploying large language models into production systems, often as APIs or services. Requires infrastructure setup, load balancing, scaling, and monitoring, to ensure reliable and efficient model serving.
Monitoring and Maintenance – Ongoing monitoring of model performance, health, and resource usage. Includes tracking metrics, detecting anomalies and triggering alerts for prompt action. Regular model updates and retraining may also be part of the maintenance process.
Security and Governance – Ensuring the security and privacy of large language models and their associated data. This includes access controls, encryption, compliance with regulatory requirements and ethical considerations like Responsible AI.
CI/CD – Adopting CI-CD practices to automate the testing, validation, and deployment of LLMs. This enables faster iterations and reduces the risk of errors in production.
Collaboration and Reproducibility – LLMOps emphasizes collaboration and reproducibility of LLMs. This includes version control, experiment tracking and documentation to enable collaboration among data scientists, engineers and researchers.
If you’re familiar with MLOps, you can see that these are key aspects of MLOps as well. In LLMOps, they are extended and adjusted to meet the requirements of LLMs.
The LLMOps Landscape
The LLMOps landscape is constantly evolving, as new tools and platforms are developed to meet the needs of organizations that are using LLMs. Some of the key players in the LLMOps landscape include:
Hugging Face – Hugging Face is a leading open-source software company that provides tools and libraries for building and using LLMs. Their Transformers library is one of the most popular libraries for LLMs, and it is used by developers and researchers around the world.
MLRun – MLRun is an open-source MLOps orchestration framework that can be used for operationalizing LLMs. It enables scaling and automation of ML and LLM pipelines in a streamlined manner. This includes ingestion, preparation and serving of data with the online and offlinefeature store, distributed ML pipelines and CI/CD automation, and elastic serving and application pipelines for real-time.
How Can MLOps Infrastructure Be Used for LLMOps?
MLOps platforms, like MLRun and Iguazio, can be used for LLMOps. To do so, some of the steps need to be adapted. For example, the embeddings, tokenization, and data cleansing steps need to be adjusted, to name a few. Validation and testing also require a different approach. However, these platforms enable the kep aspects of LLMOps: automating the flow, processing at scale, rolling upgrades, rapid pipeline development and deployment, models monitoring, and more.
Looking to practically apply LLMs? Check out this demo showing MLOps orchestration best practices for Generative AI applications.