Arize AI, a market leader in machine learning observability, debuted deeper support on the Arize platform for generative AI and a first-of-its-kind open source observability library for evaluating large language models (LLMs) at its Arize:Observe 2023 summit.
The launch comes at a critical moment for the future of AI. Generative AI is fueling a technical renaissance, with models like GPT-4 showing sparks of artificial general intelligence and new breakthroughs and use cases emerging daily. On the other hand, most leading large language models are black boxes that have known issues around hallucination and problematic biases.
Available , Arize Phoenix is the first open source observability library specifically built to help data scientists evaluate outputs from LLMs like OpenAI’s GPT-4, Google’s Bard, Anthropic’s Claude, and others. Leveraging Phoenix, data scientists can visualize complex LLM decision-making, monitor LLMs when they produce false or misleading results, and narrow in on fixes to improve outcomes.
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“A huge barrier in getting LLMs and Generative Agents to be deployed into production is because of the lack of observability into these systems,” says Harrison Chase, Co-Founder of LangChain. “With Phoenix, Arize is offering an open source way to visualize complex LLM decision-making.”
“Phoenix is a much-appreciated advancement in model observability and production,” says Christopher Brown, CEO and Co-Founder of AI-focused consulting firm Decision Patterns and a former Computer Science lecturer at UC Berkeley. “The integration of observability utilities directly into the development process not only saves time but encourages model development and production teams to actively think about model use and ongoing improvements before releasing to production. This is a big win for management of the model lifecycle.”
“Despite calls to halt AI development, the reality is that innovation will continue to accelerate,” said Jason Lopatecki, CEO and Co-Founder of Arize AI. “Phoenix is the first software designed to help data scientists understand how GPT-4 and LLMs think, monitor their responses and fix the inevitable issues as they arise.”
Phoenix is instantiated by a simple import call in a Jupyter notebook and is built to interactively run on top of Pandas dataframes. The tool works easily with unstructured text and images, with embeddings and latent structure analysis designed as a core foundation of the toolset.
SOURCE: PR Newswire