Arize AI
arize.com
Ship reliable AI agents by monitoring, evaluating, and improving them.
Description
The continual learning platform for agents. · Continuously improve AI agents with agent observability, evaluation, tracing, and experimentation. · Agent debugging needs end-to-end workflows · Infrastructure for self‑improving agents. · Your data stays yours. And stays secure. · Built on open source & open standards.
Arize AI is an ML observability platform that helps AI engineers and data scientists monitor, troubleshoot, and evaluate LLM models. It enables teams to surface model issues quickly, resolve root causes, and improve overall model performance. The platform supports continuous monitoring and improvement across the entire ML lifecycle, from deployment to production, with features for detecting drift, analyzing performance, and tracing issues back to problematic data
Features
AUTOMATED ISSUE DETECTION
ROOT CAUSE ANALYSIS
PERFORMANCE MONITORING
TRACING WORKFLOWS
EXPLORATORY DATA ANALYSIS
DYNAMIC DASHBOARDS
LLM EVALUATION FRAMEWORK
EXPERIMENT RUNS SUPPORT
CUSTOM EVALUATIONS
Use cases
DETECTING MODEL DRIFT IN PRODUCTION
ANALYZING AGGREGATE MODEL PERFORMANCE
CONDUCTING A/B PERFORMANCE COMPARISONS
MANAGING DATA QUALITY ISSUES
ANALYZING MODEL FAIRNESS METRICS
EVALUATING LLM TASK PERFORMANCE
FAQ
Arize AI is a unified AI observability platform designed to help engineers monitor, troubleshoot, and optimize machine learning and generative AI models at scale. It supports traditional ML models (like classification, regression) and modern AI systems such as generative AI and retrieval-augmented generation (RAG) chatbots.
Arize natively supports binary classification, multi-class classification, regression, ranking, natural language processing (NLP), and computer vision (CV) models. It also supports various data types including tabular/structured data (strings, floats, booleans) and embeddings for unstructured data like images and text.
To start, you set up your model by sending in training, validation, and/or production data (or a subset of these) for ingestion. You verify data ingestion in the ‘Data Ingestion’ tab, then set a performance baseline and choose metrics to monitor in the ‘Config’ tab. The platform processes and indexes data in about 10 minutes before full visibility.
You can send historical data with prediction timestamps up to 2 years old. Arize accepts null values in predictions or actuals as long as each record contains at least one non-null prediction, actual, or feature importance. New features in production are handled by monitoring data quality and drift.
Arize supports many standard metrics across model types including Accuracy, Precision, Recall, F1, AUC, Log Loss, RMSE, MAE, R-squared, and also supports custom metrics. Metrics can be evaluated in aggregate or across cohorts defined by filters.
Arize provides data quality checks, drift detection, and feature performance heatmaps to surface outliers, anomalous data, and poorly performing feature slices. It also offers root cause analysis workflows to drill down from symptoms to the underlying cause of model failures.
Phoenix is the open-source AI observability tool by Arize, focusing on introspecting and debugging interactions with LLMs and generative AI workflows. It complements Arize AX, the enterprise SaaS platform. Phoenix enables tracing of prompts, retriever performance, and pinpointing failure causes in complex AI systems.
Arize Phoenix is completely open-source and free under Apache-2.0 license. The full commercial SaaS platform, Arize AX, is enterprise-grade and paid.
Data should be correctly formatted with model IDs, prediction IDs, and timestamps. If sending delayed actuals, ensure the matching prediction row exists for proper joining. Null or missing values in features or predictions are generally accepted but must conform to minimum row completeness. The Data Ingestion tab helps verify data receipt, and troubleshooting guides are available if issues arise.
Yes, Arize can monitor performance, debug issues, and provide observability into AI agents, such as those built with Langflow, by integrating Arize Platform and Phoenix for comprehensive insights across workflows.
Specs
| Type | Agent |
| Section | Infrastructure & MLOps |
| Pricing | paid (от $50/mo) |
| Platform | Web + desktop |
| Systems | macos, cli, web |
| Hosting | self-hosted |
| Who for | Individual |
| Site language | en |
| Rating | 0.00 (0 reviews) |
| Views | 255 751 |
| Launched | 2024-11-21 |
