AI Observability
and Evaluation

Jerry Liu CEO and Co-Founder, LlamaIndex

As LLM-powered applications increase in sophistication and new use cases emerge, deeper capabilities around LLM observability are needed to help debug and troubleshoot. We’re pleased to see this open-source solution from Arize, along with a one-click integration to LlamaIndex, and recommend any AI engineers or developers building with LlamaIndex check it out.

Harrison Chase Co-Founder of LangChain

A huge barrier in getting LLMs and Generative Agents to be deployed into production is because of the lack of observability into these systems. With Phoenix, Arize is offering an open source way to visualize complex LLM decision-making.

Christopher Brown CEO and Co-Founder of Decision Patterns and a former UC Berkeley Computer Science lecturer

Phoenix is a much-appreciated advancement in model observability and production. 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.

Pietro Bolcato Lead ML Engineer, Kling Klang Klong

This is a library to LLMs and RNs that provides visual clustering analysis and model interpretability, super useful to help understand how a model works, and to demystify the black-box phenomena!

Yuki Waka Application Developer, Klick

Phoenix integrated into our team’s existing data science workflows and enabled the exploration of unstructured text data to identify root causes of unexpected user inputs, problematic LLM responses, and gaps in our knowledge base.

Lior Sinclair AI Researcher

Just came across Arize-phoenix, a new library for LLMs and RNs that provides visual clustering and model interpretability. Super useful.

Tom Matthews Machine Learning Engineer at Unitary.ai

This is something that I was wanting to build at some point in the future, so I’m really happy to not have to build it. This is amazing.

Erick Siavichay Project Mentor, Inspirit AI

We are in an exciting time for AI technology including LLMs. We will need better tools to understand and monitor an LLM’s decision making. With Phoenix, Arize is offering an open source way to do exactly just that in a nifty library.

Shubham Sharma, VentureBeat

Large language models...remain susceptible to hallucination — in other words, producing false or misleading results. Phoenix, announced today at Arize AI’s Observe 2023 summit, targets this exact problem by visualizing complex LLM decision-making and flagging when and where models fail, go wrong, give poor responses or incorrectly generalize.

Yujian Tang Published in Plain Simple Software

23 Open Source AI Libraries for 2023. AI may be the top field to get into in 2023. Here are 23 open source libraries to get you started.

Jerry Liu CEO and Co-Founder, LlamaIndex

As LLM-powered applications increase in sophistication and new use cases emerge, deeper capabilities around LLM observability are needed to help debug and troubleshoot. We’re pleased to see this open-source solution from Arize, along with a one-click integration to LlamaIndex, and recommend any AI engineers or developers building with LlamaIndex check it out.

Harrison Chase Co-Founder of LangChain

A huge barrier in getting LLMs and Generative Agents to be deployed into production is because of the lack of observability into these systems. With Phoenix, Arize is offering an open source way to visualize complex LLM decision-making.

Christopher Brown CEO and Co-Founder of Decision Patterns and a former UC Berkeley Computer Science lecturer

Phoenix is a much-appreciated advancement in model observability and production. 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.

Pietro Bolcato Lead ML Engineer, Kling Klang Klong

This is a library to LLMs and RNs that provides visual clustering analysis and model interpretability, super useful to help understand how a model works, and to demystify the black-box phenomena!

Yuki Waka Application Developer, Klick

Phoenix integrated into our team’s existing data science workflows and enabled the exploration of unstructured text data to identify root causes of unexpected user inputs, problematic LLM responses, and gaps in our knowledge base.

Lior Sinclair AI Researcher

Just came across Arize-phoenix, a new library for LLMs and RNs that provides visual clustering and model interpretability. Super useful.

Tom Matthews Machine Learning Engineer at Unitary.ai

This is something that I was wanting to build at some point in the future, so I’m really happy to not have to build it. This is amazing.

Erick Siavichay Project Mentor, Inspirit AI

We are in an exciting time for AI technology including LLMs. We will need better tools to understand and monitor an LLM’s decision making. With Phoenix, Arize is offering an open source way to do exactly just that in a nifty library.

Shubham Sharma, VentureBeat

Large language models...remain susceptible to hallucination — in other words, producing false or misleading results. Phoenix, announced today at Arize AI’s Observe 2023 summit, targets this exact problem by visualizing complex LLM decision-making and flagging when and where models fail, go wrong, give poor responses or incorrectly generalize.

Yujian Tang Published in Plain Simple Software

23 Open Source AI Libraries for 2023. AI may be the top field to get into in 2023. Here are 23 open source libraries to get you started.