01.04.2024
New Releases, Enhancements, + Changes
Last updated
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New Releases, Enhancements, + Changes
Last updated
Was this helpful?
Analyze performance by hour, day, week or month using the new granularity selector. Options default to the optimal granularity for a given time range. The granularity selector is available on the monitors page, all the relevant model pages, and the dashboards page.
Users can now view the performance of individual feature slices over time. In the performance tracing tab, select a feature and click "Slice Performance Over Time". This will plot the performance of the top five slices of that feature. Correlate performance issues with individual slices, further debug your features, or better understand trends over time.
The generative LLM model overview page has undergone a couple of changes to increase ease of use:
Quick Actions cards linking to various in app workflows
Model Schema is now included on the left side navigation
This release enables latent actuals for generative LLM models. In addition, users will now be alerted when their data files are too large.
This release brings to life a new model type: multi-class. Users can now log not only the classes by their classification models, but also the non-selected ones, giving a detailed view of how the model operates. Currently, only the record-at-a-time logging option is supported, but stay tuned for the release of pandas batch ingestion.
The latest ebooks, self-guided course modules, and technical posts on topics like LLM evaluation and beyond:
Users can now utilize Google's new Gemini model via the Vertex AI integration in Arize's .
Check out LLM Tracing via Phoenix's UI via . For more info, .
: everything you need to know
: Benchmarking, Explanations
& Evaluating Prompts
with AI: A Study in Zero-shot, Single-domain, and Cross-domain Settings
: A Deep Dive Into the Latest Models
