LLMOps
LLMOps is an evolving subfield of MLOps that focuses on operationalizing large language models(LLMs) at scale.
What is LLMOps?
LLMOps is a subfield or specific use case of MLOps that is evolving and focussed on the operationalization of large language models(LLMs).
Like MLOps, it involves streamlining models, specifically LLMs, facilitating tools and workflows to train, deploy and manage large language models seamlessly.
Microsoft used the term LMOps earlier as 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 for the sake of convenience, let's stick with LLMOps only.
Why LLMOps?
Leveraging LLM capabilities for businesses requires sophisticated and expensive infrastructure. Only OpenAI and some specific enterprises have successfully brought these models to market.
Challenges in productizing LLMs include:
Model size: LLMs have billions of parameters requiring specialised computational resources and capabilities. As a result, LLMs management becomes time-consuming and expensive.
Complex datasets: Complex and large datasets management is a crucial challenge to LLM practitioners. Its development requires a massive amount of data to be trained and lots of parallel processing and optimization.
Continuous monitoring & evaluation: Here, LLMs are similar to their cousins – ML models. They should be monitored and evaluated continuously using regular testing and different metrics.
Model optimization: Large language models require continuous retraining and feedback loops to fine-tune them. With LLMOps, you can optimise large foundational models through transfer learning that helps leverage LLM capabilities for specific, less-computational tasks.
For example, while GPT-3 is incredibly powerful, it is not necessarily the most efficient solution for every NLP task. Transfer learning involves taking a pre-trained language model, like GPT-3, and fine-tuning it on a specific task or domain with a smaller dataset.
But the challenge is facilitating infrastructure for parallel GPU processing and handling massive datasets.
And to address these challenges, LLMOps comes into the picture. Although currently in the research phase, it is helping enterprises to operationalize their generative AI models.
Practising LLMOps for Generative AI Success
The LLMOps tools landscape is constantly evolving as new tools and frameworks are being developed to support the operationalization of large language models. Some prominent tooling options are LangChain, Humanloop, Attri, OpenAI GPT, and Hugging Face. While these tools support various stages of the LLM lifecycle, Censius helps you bring post-deployment observability to your large language models. The platform encourages businesses to watch impacting factors like latency, tokens usage, output validation, and versioning, among others.
Continuous and automated monitoring of large language models is essential to ensure their performance over time. LLM monitoring solutions address unique aspects such as tracking prompts, fine-tuning experiments, computing production model performance, and automatic retraining of models based on the requirements. Once you integrate an LLM observability platform such as Censius, you can stay ahead of risks such as model drift.
Here’s a collection of some of the tools and platforms for LLMOps:
Further Reading
https://github.com/tensorchord/Awesome-LLMOps#model-management
DevTools for language models — predicting the future