Navigating the evolving LLMOps landscape
This article is co-authored by Cam YoungSenior Global Solutions Architect at Arize AI
From better customer service to faster drug discovery, generative AI is rapidly reshaping industries. According to a recent survey, 61.7% of enterprise engineering teams now have or plan to deploy a large real-world language model application within a year – with more than one in ten (14, 7%) already in production, compared to 8.3%. in April.
Survey: production deployment plans for LLMs
Among early adopters of LLMs, nearly half (43%) cite issues such as grading, hallucinations, and unnecessary abstraction as implementation challenges. How can large companies overcome these challenges to achieve results and minimize organizational risks?
Here are three keys that companies successfully deploying LLMs are adopting to meet the challenge.
Taking an agnostic approach to a changing landscape
A team of engineers who spend a month building infrastructure that only connects to a single base model (i.e. OpenAI’s GPT-4) or orchestration framework (i.e. i.e. LangChain) can quickly see its work – or even its entire business strategy – rendered obsolete. Ensure that a company AI Observability and the stack is agnostic and easily connects to major core models and tools, helping to minimize switching costs and friction.
Operationalization of scientific experiments LLM
In a space where core model providers offer their own assessments (effectively grading their own assignments), it is important to develop or leverage independent assessment tools. LLM Assessment. This objectivity – combined with a team of data scientists and machine learning platform engineers – can provide a solid foundation for organizations to quickly automate and operationalize hundreds of scientific experiments for LLM use cases, ensuring the production reliability and responsible use of AI across the business.
Quantify return on investment and productivity gains
Implementing generative AI can be difficult and time-consuming given the complexity and novelty of the models. It is important to ensure that systems exist to detect revenue-impacting LLM application performance issues – with associated workflows to proactively and automatically surface the root cause. Here, open source and other tools can help minimize disruption through interactive and guided workflows such as UMAP, extents and traces, and invite playing fields to compare. prompt template answers, and more.
Conclusion
As the field of generative AI continues to evolve, it can be difficult to balance the obligation to deploy LLM applications reliably and responsibly with the need for speed given today’s unique competitive pressures. Hopefully, these three keys for leaders to navigate the vast landscape of language model operations can help us as we head into a new year – and a new era!