Arize is the Leader in AI observability empowering teams to monitor LLMs, surface issues and troubleshoot LLM / ML applications in production. The ability to run Evaluations,

The ability to easily surface unknown issues and diagnose the root cause is what differentiates machine learning observability from traditional model monitoring tools. By centralizing datasets across your training, validation, and production environments across all versions, Arize provides ML & AI teams with the ability to gain the model visibility necessary to detect issues, troubleshoot why they happened, and improve overall model performance.

LLM evaluation with explanations – simple, fast, and accurate LLM-based evaluations for a variety of tasks including code generation, context relevance, hallucination, Q&A correctness, summarization, and toxicity.

Retrieval Augmented Generation (RAG) troubleshooting – RAG troubleshooting flows that allow teams to visualize both the embeddings of chunks and embeddings of queries. These workflows enable teams debugging capabilities to track down problematic groups of queries in RAG systems.

Troubleshoot LLM traces and spans – visibility into where conversational and agent based workflows across different span types, with support for LangChain, LlamaIndex, and LLM Otel

Native support for any ML model type or LLM provider – including ranking models, computer vision, NLP. For LLMs Arize supports OpenAI, Cohere, Bedrock, PaLM 2, and more.

Find and fix model problems faster with Performance Tracing – Quickly unmask and troubleshoot hidden issues with our unique prediction slicing and filtering capabilities. Specific cohorts of problematic predictions are highlighted for your attention, with tools to see the exact features and dimensions that are pulling performance down.

Real-time monitoring designed for scale – Monitors for drift, data quality, and performance are automatically created for every model. A central model health hub automatically surfaces potential issues with performance and data, sending real-time alerts so you can take immediate action.

Pinpoint drift across thousands of features – Track for prediction, data, and concept drift across any model facet or combination of dimensions. Easily compare evaluation datasets across training, validation, and production environments to determine any changes against a baseline reference – with lookback windows down to the hourly level.

Keep data integrity in check – Ensure the quality of model data inputs and outputs with automated checks for missing, unexpected, or extreme values. Out-of-distribution points can be separated for root cause analysis and to better understand the impact on aggregate performance.

Improve interpretability and explainability – Gain insights into how your models arrive at outcomes to optimize performance over time. See how a model dimension affects prediction distributions, and leverage SHAP to explain feature importance for specific cohorts.

Powerful, dynamic data visualization – Leverage pre-configured dashboard templates when you need to quickly view the health of your models or create customized dashboards for ad hoc analysis. Visualizations of statistical distributions and performance heatmaps help focus your troubleshooting efforts.

Collaborate worry-free with enterprise-grade control – Easily send in billions of events daily, across any model without latency concerns, and ensure your account is set-up for secure collaboration with configurable organizations, spaces, projects, and role-based access controls.

Details on all Arize plans can be found at https://arize.com/pricing/. Pricing for Arize Pro is available under the Plans + Pricing section. Contact us directly for custom offers for Arize Starter Edition & Enterprise at marketplace@arize.com

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Arize AI

Arize AI

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