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    • Evaluation
  • 🧪Develop
    • Quickstart: Experiments
    • Datasets
      • Create a dataset
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      • Export a dataset
    • Experiments
      • Run experiments
      • Run experiments with code
        • Experiments SDK differences in AX vs Phoenix
        • Log experiment results via SDK
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      • Evaluate experiment with code
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        • Github Action Basics
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      • Download experiment
    • Prompt Playground
      • Use tool calling
      • Use image inputs
      • Replay spans
      • Compare prompts side-by-side
      • Load a dataset into playground
      • Save playground outputs as an experiment
      • ✨Copilot: prompt builder
    • Playground Integrations
      • OpenAI
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      • Custom LLM Models
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  • 🧠Evaluate
    • Online Evals
      • Run evaluations in the UI
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  • 🔭Observe
    • Quickstart: Tracing
    • Tracing
      • Setup tracing
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        • Trace inputs and outputs
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        • Send Traces from Phoenix -> Arize
        • Advanced Tracing (OTEL) Examples
      • Add metadata
        • Add events, exceptions and status
        • Logging Latent Metadata
        • Add attributes, metadata and tags
        • Send data to a specific project
        • Get the current span context and tracer
      • Configure tracing options
        • Configure OTEL tracer
        • Mask span attributes
        • Redact sensitive data from traces
        • Instrument with OpenInference helpers
      • Query traces
        • Filter Traces
          • Time Filtering
        • Export Traces
        • ✨AI Powered Search & Filter
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    • Tracing Integrations
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      • Amazon Bedrock Agents
      • MistralAI
      • Anthropic
      • LangFlow
      • Haystack
      • LiteLLM
      • CrewAI
      • Groq
      • DSPy
      • Guardrails AI
      • Prompt flow
      • Vercel AI SDK
      • Llama
      • Together AI
      • OpenTelemetry (arize-otel)
      • BeeAI
    • Evals on Traces
    • Guardrails
    • Sessions
    • Dashboards
      • Dashboard Widgets
      • Tracking Token Usage
      • ✨Copilot: Dashboard Widget Creation
    • Monitors
      • Integrations: Monitors
        • Slack
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        • OpsGenie
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      • LLM Red Teaming
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      • Arize Query Language Syntax
        • Conditionals and Filters
        • All Operators
        • All Functions
      • Custom Metric Examples
      • ✨Copilot: ArizeQL Generator
  • 📈Machine Learning
    • Machine Learning
      • User Guide: ML
      • Quickstart: ML
      • Concepts: ML
        • What Is A Model Schema
        • Delayed Actuals and Tags
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      • How To: ML
        • Upload Data to Arize
          • Pandas SDK Example
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          • Troubleshoot Data Upload
          • Sending Data FAQ
        • Monitors
          • ML Monitor Types
          • Configure Monitors
            • Notifications Providers
          • Programmatically Create Monitors
          • Best Practices for Monitors
        • Dashboards
          • Dashboard Widgets
          • Dashboard Templates
            • Model Performance
            • Pre-Production Performance
            • Feature Analysis
            • Drift
          • Programmatically Create Dashboards
        • Performance Tracing
          • Time Filtering
          • ✨Copilot: Performance Insights
        • Drift Tracing
          • ✨Copilot: Drift Insights
          • Data Distribution Visualization
          • Embeddings for Tabular Data (Multivariate Drift)
        • Custom Metrics
          • Arize Query Language Syntax
            • Conditionals and Filters
            • All Operators
            • All Functions
          • Custom Metric Examples
          • Custom Metrics Query Language
          • ✨Copilot: ArizeQL Generator
        • Troubleshoot Data Quality
          • ✨Copilot: Data Quality Insights
        • Explainability
          • Interpreting & Analyzing Feature Importance Values
          • SHAP
          • Surrogate Model
          • Explainability FAQ
          • Model Explainability
        • Bias Tracing (Fairness)
        • Export Data to Notebook
        • Automate Model Retraining
        • ML FAQ
      • Use Cases: ML
        • Binary Classification
          • Fraud
          • Insurance
        • Multi-Class Classification
        • Regression
          • Lending
          • Customer Lifetime Value
          • Click-Through Rate
        • Timeseries Forecasting
          • Demand Forecasting
          • Churn Forecasting
        • Ranking
          • Collaborative Filtering
          • Search Ranking
        • Natural Language Processing (NLP)
        • Common Industry Use Cases
      • Integrations: ML
        • Google BigQuery
          • GBQ Views
          • Google BigQuery FAQ
        • Snowflake
          • Snowflake Permissions Configuration
        • Databricks
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        • Azure Blob Storage
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          • Private Image Link Access Via AWS S3
        • Kafka
        • Airflow Retrain
        • Amazon EventBridge Retrain
        • MLOps Partners
          • Algorithmia
          • Anyscale
          • Azure & Databricks
          • BentoML
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          • Deepnote
          • Feast
          • Google Cloud ML
          • Hugging Face
          • LangChain 🦜🔗
          • MLflow
          • Neptune
          • Paperspace
          • PySpark
          • Ray Serve (Anyscale)
          • SageMaker
            • Batch
            • RealTime
            • Notebook Instance with Greater than 20GB of Data
          • Spell
          • UbiOps
          • Weights & Biases
      • API Reference: ML
        • Python SDK
          • Pandas Batch Logging
            • Client
            • log
            • Schema
            • TypedColumns
            • EmbeddingColumnNames
            • ObjectDetectionColumnNames
            • PromptTemplateColumnNames
            • LLMConfigColumnNames
            • LLMRunMetadataColumnNames
            • NLP_Metrics
            • AutoEmbeddings
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
          • Single Record Logging
            • Client
            • log
            • TypedValue
            • Ranking
            • Multi-Class
            • Object Detection
            • Embedding
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            • utils.types.ModelTypes
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        • Java SDK
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          • logTrainingRecords
        • R SDK
          • Client$new()
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        • Rest API
    • Computer Vision
      • How to: CV
        • Generate Embeddings
          • How to Generate Your Own Embedding
          • Let Arize Generate Your Embeddings
        • Embedding & Cluster Analyzer
        • ✨Copilot: Embedding Summarization
        • Similarity Search
        • Embedding Drift
        • Embeddings FAQ
      • Integrations: CV
      • Use Cases: CV
        • Image Classification
        • Image Segmentation
        • Object Detection
      • API Reference: CV
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On this page
  • Arize AX Features
  • Quickstarts
  • Next Steps
  • Machine Learning Guides

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Arize AI

AI Engineering Platform

Last updated 22 days ago

Was this helpful?

Arize is an AI engineering platform focused on evaluation and observability. It helps engineers develop, evaluate, and observe AI applications and agents.

Arize has both Enterprise and OSS products to support this goal:

  • Arize AX — an enterprise AI engineering platform from development to production, with an embedded

  • — a lightweight, open-source project for tracing, prompt engineering, and evaluation

  • — an open-source instrumentation package to trace LLM applications across models and frameworks

We log over 1 trillion inferences and spans, 10 million evaluation runs, and 2 million OSS downloads every month.

Arize AX Features

Iterate on prompts

  • - compare different prompts side by side

  • - manage and version your prompts in one place

  • - generate prompts with AI

  • - systematically A/B test prompts against large datasets

Run experiments

Trace your application

Evaluate performance

Build AI with AI

Quickstarts

Running Arize AX for the first time? Select a quickstart below.

Next Steps

Check out a comprehensive list of example notebooks for agents, RAG, voice, tracing, evals, and more.

See our video deep dives on the latest papers in AI.

Join the Arize Slack community to ask questions, share findings, provide feedback, and connect with other developers.

Machine Learning Guides

Looking for help with predictive machine learning or computer vision models guides? Start here:

- create and update test datasets to measure performance

- store every experiment run in a structured format

- systematically measure performance improvements based on LLM and code evaluations

- gate deployment to production based on experiment performance

- get instant visibility into your application traces

- use our search and filter capabilities to find outliers of poor performance

- determine the causes of poor performance across hundreds of spans

- run evals continuously against your data

- create custom dashboards to monitor performance

- get alerts when performance deviates from the norm

- prevent poor performing outputs from reaching users

- use labeling queues to run evals and annotate your spans in one place

- find patterns in your data

- write tailored evals based on custom criteria

- analyze your document retrieval and suggest improvements

- analyze and evaluate any span in chat

- generate dashboard widgets with natural language

- get suggested prompt edits based on best practices

Curate datasets
Track experiments
Evaluate experiments
CI/CD
Setup tracing instrumentation
Find problematic traces
Run quick evaluations
Powered by Phoenix OSS
Evaluate production data
Track key metrics
Get alerts
Guardrail bad outputs
Annotate your outputs
AI Search
Create Custom Evaluations
Diagnose RAG Issues
Span Chat
Dashboard Generator
Optimize Prompts
And more!
Watch our paper readings
Join our Slack community

Machine Learning

Log inferences and debug your machine learning models

Computer Vision

Run similarity search and evaluate performance of your CV models

Phoenix
OpenInference
Prompt playground
Prompt hub
Prompt builder
Save as experiment
AI Copilot
Try our tutorials

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Run experiments

Systematically experiment and evaluate performance against your data.

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Trace your application

Get visibility into your application traces using our auto-instrumentation plugins.

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Iterate on prompts

Iterate on prompt templates, input variables, LLM models, and parameters without code.

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Run evaluations

Run LLM or code evaluations continuously against your data.