LogoLogo
Python SDKSlack
  • Documentation
  • Cookbooks
  • Self-Hosting
  • Release Notes
  • Reference
  • Arize AI
  • Quickstarts
  • ✨Arize Copilot
  • Arize AI for Agents
  • Concepts
    • Agent Evaluation
    • Tracing
      • What is OpenTelemetry?
      • What is OpenInference?
      • Openinference Semantic Conventions
    • Evaluation
  • 🧪Develop
    • Quickstart: Experiments
    • Datasets
      • Create a dataset
      • Update a dataset
      • Export a dataset
    • Experiments
      • Run experiments
      • Run experiments with code
        • Experiments SDK differences in AX vs Phoenix
        • Log experiment results via SDK
      • Evaluate experiments
      • Evaluate experiment with code
      • CI/CD with experiments
        • Github Action Basics
        • Gitlab CI/CD Basics
      • 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
      • Azure OpenAI
      • AWS Bedrock
      • VertexAI
      • Custom LLM Models
    • Prompt Hub
  • 🧠Evaluate
    • Online Evals
      • Run evaluations in the UI
      • Run evaluations with code
      • Test LLM evaluator in playground
      • View task details & logs
      • ✨Copilot: Eval Builder
      • ✨Copilot: Eval Analysis
      • ✨Copilot: RAG Analysis
    • Experiment Evals
    • LLM as a Judge
      • Custom Eval Templates
      • Arize Templates
        • Agent Tool Calling
        • Agent Tool Selection
        • Agent Parameter Extraction
        • Agent Path Convergence
        • Agent Planning
        • Agent Reflection
        • Hallucinations
        • Q&A on Retrieved Data
        • Summarization
        • Code Generation
        • Toxicity
        • AI vs Human (Groundtruth)
        • Citation
        • User Frustration
        • SQL Generation
    • Code Evaluations
    • Human Annotations
  • 🔭Observe
    • Quickstart: Tracing
    • Tracing
      • Setup tracing
      • Trace manually
        • Trace inputs and outputs
        • Trace function calls
        • Trace LLM, Retriever and Tool Spans
        • Trace prompt templates & variables
        • Trace as Inferences
        • 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
        • ✨AI Powered Trace Analysis
        • ✨AI Span Analysis & Evaluation
    • Tracing Integrations
      • OpenAI
      • OpenAI Agents SDK
      • LlamaIndex
      • LlamaIndex Workflows
      • LangChain
      • LangGraph
      • Hugging Face smolagents
      • Autogen
      • Google GenAI (Gemini)
      • Model Context Protocol (MCP)
      • Vertex AI
      • Amazon Bedrock
      • 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
          • Manual Setup
        • OpsGenie
        • PagerDuty
      • LLM Red Teaming
    • Custom Metrics & Analytics
      • 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
        • ML Glossary
      • How To: ML
        • Upload Data to Arize
          • Pandas SDK Example
          • Local File Upload
            • File Upload FAQ
          • Table Ingestion Tuning
          • Wildcard Paths for Cloud Storage
          • 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
        • Google Cloud Storage (GCS)
        • Azure Blob Storage
        • AWS S3
          • Private Image Link Access Via AWS S3
        • Kafka
        • Airflow Retrain
        • Amazon EventBridge Retrain
        • MLOps Partners
          • Algorithmia
          • Anyscale
          • Azure & Databricks
          • BentoML
          • CML (DVC)
          • 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
            • LLMRunMetadata
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
        • Java SDK
          • Constructor
          • log
          • bulkLog
          • logValidationRecords
          • logTrainingRecords
        • R SDK
          • Client$new()
          • Client$log()
        • 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
Powered by GitBook

Support

  • Chat Us On Slack
  • support@arize.com

Get Started

  • Signup For Free
  • Book A Demo

Copyright © 2025 Arize AI, Inc

On this page
  • Overview
  • Authentication
  • Full Record
  • Supported label types
  • Request Example (numeric label)
  • Actual
  • Supported label types
  • Request Example (score categorical label)

Was this helpful?

  1. Machine Learning
  2. Machine Learning
  3. API Reference: ML

Rest API

Arize AI for Model Monitoring, Troubleshooting, and Explainability

Overview

Arize's APIs are the main ingresses for data which powers our platform. Most applications will use an existing SDK in the language of your choice, but it's important to know what the underlying API looks like first in case you want to work in a language without a current SDK implementation.

Authentication

Arize uses API keys in conjunction with a Space id in order to authenticate request. You will receive your keys once you sign up for our product. Your API and Space id are the keys to your data castle so be sure to keep them secure. Do not share your keys in public forums including Github, StackOverflow, logs, etc. Both the API key and Space ID are is authenticated via auth headers, use -H 'Authorization: API_KEY_VALUE' -H 'Grpc-Metadata-space_id: SPACE_ID' -H 'Grpc-Metadata-sdk-language: rest

Arize uses conventional HTTP response codes to convey resulting success or failure of a given request. Codes in the 2XX range indicate success, 4XX range indicate content failure, 5XX indicate errors with Arize's edge servers.

Status Code
Message
Reason

200

OK

Success

403

cannot access request headers

Failure when accessing incoming request's headers

403

must provide authorization header�

Missing Authorization header

403

unable to validate authorization header

Space id is not valid

403

invalid api-key

API Key is not valid

400

log must include prediction or actual

Prediction or Actual is a required field and it's not present

400

invalid feature type

Feature data type is not supported

400

timestamp must range from now back to T-90 days

Record timestamp can only be backdated up to 90 days

500

Internal service error, contact Arize

Internal error processing messages - Contact Arize

Full Record

To send an individual prediction record, you should hit Arize's log API https://api.arize.com/v1/log

Supported label types

Label
data type

"numeric"

double

"score categorical"

Tuple (string, float)

Request Example (numeric label)

curl --request POST 'https://api.arize.com/v1/log' \
--header 'Authorization: API_KEY' \
--header 'Grpc-Metadata-space_id: SPACE_ID' \
--header 'Grpc-Metadata-sdk-language: rest' \
--data-raw '{
   "model_id":"example_model_id",
   "prediction_id":"ef00f52c-d6d4-4e48-84cd-70166b3f423f",
   "prediction":{
      "timestamp":"2020-10-02T00:37:30.208687Z",   //Optional, defaults to now()
      "model_version":"v0.1",
      "label":{
         "numeric":0.1
      },
      "features":{
         "feature_1_float":{
            "double":0.6604474844066184
         },
         "feature_2_str":{
            "string":"str val"
         },
         "feature_4_bool":{
            "string":"True"
         },
         "feature_2_float":{
            "double":0.02876647860632975
         },
         "feature_3_float":{
            "double":0.3358383777892534
         },
         "feature_0_np_ll":{
            "int":"77"
         },
         "feature_3_bool":{
            "string":"True"
         },
         "feature_1_str":{
            "string":"str val"
         },
         "feature_1_np_ll":{
            "int":"1"
         },
         "feature_4_float":{
            "double":0.9681327640084113
         },
         "image_embedding":{
            "embedding":{
               "vector":[
                  1.0,
                  2.0,
                  3.0
               ],
               "link_to_data":"https://my-bucket.s3.us-west-2.amazonaws.com/puppy.png"
            }
         },
         "feature_2_bool":{
            "string":"True"
         },
         "feature_2_np_ll":{
            "int":"91"
         },
         "feature_0_str":{
            "string":"str val"
         },
         "feature_4_np":{
            "double":0.543297819558724
         },
         "feature_1_bool":{
            "string":"True"
         },
         "feature_3_np":{
            "double":0.5149428483266209
         },
         "feature_4_str":{
            "string":"str val"
         },
         "nlp_embedding_sentence":{
            "embedding":{
               "vector":[
                  4.0,
                  5.0,
                  6.0,
                  7.0
               ],
               "link_to_data":"",
               "raw_data":{
                  "tokenArray":{
                     "tokens":[
                        "This is a test sentence"
                     ]
                  }
               }
            }
         },
         "feature_2_np":{
            "double":0.29966708188607294
         },
         "feature_3_np_ll":{
            "int":"46"
         },
         "feature_1_np":{
            "double":0.8751167134832335
         },
         "feature_0_np":{
            "double":0.3854629460534247
         },
         "feature_0_bool":{
            "string":"True"
         },
         "feature_4_np_ll":{
            "int":"74"
         },
         "feature_0_float":{
            "double":0.5696514798125208
         },
         "feature_3_str":{
            "string":"str val"
         },
         "nlp_embedding_tokens":{
            "embedding":{
               "vector":[
                  4.0,
                  5.0,
                  6.0,
                  7.0
               ],
               "link_to_data":"",
               "raw_data":{
                  "tokenArray":{
                     "tokens":[
                        "This",
                        "is",
                        "a",
                        "test",
                        "token",
                        "array"
                     ]
                  }
               }
            }
         }
      },
      "tags":{
         "tag_str":{
            "string":"arize"
         },
         "tag_int":{
            "int":"0"
         },
         "tag_double":{
            "double":20.2
         },
         "tag_bool":{
            "string":"True"
         }
      }
   },
   "actual":{
      "label":{
         "numeric":0.1
      },
      "tags":{
         "tag_str":{
            "string":"arize"
         },
         "tag_int":{
            "int":"0"
         },
         "tag_double":{
            "double":20.2
         },
         "tag_bool":{
            "string":"True"
         }
      }
   },
   "environment_params":{
      "production":{
         
      }
   }
}'

Actual

To send an individual actual record, you should hit Arize's log API https://api.arize.com/v1/log

Supported label types

Label
data type

"numeric"

double

"score categorical"

Tuple (string, float)

Request Example (score categorical label)

curl --request POST 'https://api.arize.com/v1/log' \
--header 'Authorization: API_KEY' \
--header 'Grpc-Metadata-space_id: SPACE_ID' \
--header 'Grpc-Metadata-sdk-language: rest' \
--data-raw '
{
   "model_id":"example_model_id",                          //Required
   "prediction_id":"027ed30c-6333-4eab-9492-61e117b1b46f", //Required
   "actual":{                                              //Required
      "label":{
         "score_categorical":{
            "score_category":{
               "category":"orange",
               "score":1.0
            }
         }
      }
   },
   "environment_params":{                                  //Required
      "production":{
         
      }
   }
}'

Last updated 7 months ago

Was this helpful?

Questions? Email us at or in the #arize-support channel

📈
support@arize.com
Slack us