LLMOps

What is LLMOps?

Welcome to the forefront of innovation in AI operations, where a new subfield is emerging to revolutionize the way we operationalize large language models (LLMs). Get ready to embark on a captivating journey into the world of LLMOps.

LLMOps is an emerging subfield of MLOps focused on specifically operationalizing large language models(LLMs). It involves streamlining large language models and facilitating tools and workflows to train, deploy and manage LLMs seamlessly. 

Microsoft introduced the term LMOps for a compilation of research papers catering to foundation model applications. LMOps has a broader research perspective in building AI products with foundation models, especially on the general technology for enabling AI capabilities with LLMs and Generative AI models. But we will stick with the term LLMOps only. 

Why LLMOps?

Applying LLMs capabilities for businesses demands sophisticated and expensive infrastructure. As a result, only OpenAI and some specific players could bring these models to market. 

Challenges in operationalizing LLMs include:  

• Large model size: LLMs have billions of parameters requiring specialized computational resources and capabilities. Hence, LLM management becomes time-consuming and expensive. 
• Complex datasets: Complex and large datasets management is a critical challenge to practitioners. Its development involves a massive amount of data for training and lots of parallel processing and optimization. 
• Continuous monitoring & evaluation: LLMs are just similar to their cousins – ML models. They should be monitored and evaluated continuously across different metrics. 
• Scalability: With a growing demand for LLMs, enterprises need robust infrastructure supporting these models at scale. Cloud-based solutions serve as an excellent option for scalable infrastructure to operationalize LLMs.  
• Model optimization: LLMs require continuous retraining and feedback loops to fine-tune and optimize them. With LLMOps, you can optimize large foundational models through transfer learning that helps leverage LLM capabilities for targeted operations. 

For example, while GPT-3 is extremely powerful, it is not necessarily the most efficient solution for every NLP task. Transfer learning involves using a pre-trained language model, like GPT-3, and fine-tuning it on a specific task or domain with a smaller dataset. 

LLM practitioners face the challenge of facilitating infrastructure for parallel GPU processing and handling massive datasets. LLMOps, a nascent field, addresses these challenges by streamlining LLM management, enabling transfer learning for specific tasks, and providing infrastructure for parallel processing. LLMOps empowers enterprises to harness the capabilities of LLMs and operationalize their generative AI models for transformative business outcomes.

LLMOps Toolstack You Are Looking For 

The LLMOps tools landscape constantly evolves as new tools are being developed to support large language models. Some notable options are LangChain, Humanloop, OpenAI GPT, and Hugging Face. 

Attri empowers you to build and deploy your large language and foundation models. With our FMOps platform, you can take control of your LLM journey and unlock its true potential.

And that's not all. We also understand the importance of post-deployment observability for ML applications. That's why Censius & Attri, our AI observability platform, brings unparalleled insights into your LLMs. Monitor crucial factors such as latency, token usage, output validation, and versioning, ensuring optimal performance and delivering exceptional user experiences. Step into the world of LLMOps tools and elevate your LLM capabilities to new heights.

Here's a collection of some of the tooling options available for LLMOps: 

Further Reading 

Microsoft Open Sources LMOps: A New Research Initiative to Enable Applications Development with Foundation Models, Part I

Tensorchord: GitHub Article

Attri WiKi

DevTools for language models — predicting the future