Arize AI
AI Observability and LLM evaluation
Arize helps AI engineers monitor, troubleshoot, and evaluate their AI applications and ship them to production with confidence.
It helps engineers working with large language models, traditional machine learning, or computer vision to:
Evaluate performance of their applications
Detect and troubleshoot issues
Find paths to improve performance in production
Arize offers teams of all sizes a single AI Observability platform
Core platform features
For LLM (large language model) applications:
Arize enables new workflows for LLM app developers. Trace your application, evaluate your performance, and iterate on your prompts until you can deploy to production with confidence.
We help you with the following features:
Tracing out of the box with automatic instrumentation
LLM evaluation framework that runs at scale with explanations for easier troubleshooting
Prompt engineering tools for easy iteration and testing
Production monitoring for evaluation, latency, token counting, and more
Retrieval troubleshooting with embeddings
Fully open source ecosystem across Evals, Inferences, and Tracing
Dataset creation for repeated evaluation runs *coming soon
For predictive machine learning models:
Performance tracing to surface low-performing slices of predictions
Data quality troubleshooting to detect shifts in upstream data and underlying changes
Automated monitoring across a variety of performance and custom metrics with integrations into Slack, PagerDuty, and more
Dynamic dashboards to keep stakeholders in the loop, identify trends, and check on key metrics
Model explainability to understand why your model produced its predictions
Bias tracing to identify and ensure that model bias issues across sensitive attributes such as race and sex are caught
For computer vision models:
Monitor embedding drift to identify images in production that the model wasn't trained on, or issues with data quality such as blurry, rotated, cropped images
Interactive 2D and 3D UMAP visualizations to identify problematic clusters for relabeling
Colorize and filter data to identify patterns or the structure in the data
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