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Arize AI
What is ML Observability?
Quickstart
Examples
🪄
Sending Data
Quickstart To Send Data
Model Schema
UI Drag & Drop
Python Pandas SDK
Google Cloud Storage (GCS)
AWS S3
Azure Blob Storage
Google BigQuery
Databricks
Snowflake
Sending Data FAQ
🔢
Model Types
Binary Classification
Multi-Class Classification
Regression
Timeseries Forecasting
Ranking
Natural Language Processing (NLP)
CV Classification
Large Language Models (LLM)
🔎
Tracing & Troubleshooting
Performance Tracing
Drift Troubleshooting
Data Quality Troubleshooting
Bias Tracing (Fairness)
🔔
Monitors
Choosing Your Metrics
How To Set Up Monitors
How To Set A Baseline
Alerts & Integrations
🖌
Embeddings
What is an Embedding?
How to Generate Your Own Embedding
Let Arize Generate Your Embeddings
Embedding Drift
UMAP & Cluster Analysis
Embeddings for Tabular Data - Multivariate Drift
Embeddings FAQ
🎨
dashboards
Create A Dashboard
🧙♂
Explainability
Model Explainability
SHAP
Surrogate Model
🧩
API Reference
Python Pandas (batch)
Python Single Record
Java SDK
R SDK
Rest API
Custom Metrics Query Language
GraphQL API
Arize Data APIs
🏡
On-Premise Deployment
Overview
Requirements
Installation
🔑
Admin
SSO & RBAC (Role Based Access Control)
📚
Resources
Product FAQ
Glossary
ML Platforms
Algorithmia
Azure & Databricks
BentoML
CML (DVC)
Deepnote
Kafka
Feast
Google Cloud ML
Hugging Face
MLflow
Neptune
Paperspace
PySpark
Ray Serve (Anyscale)
SageMaker
Spell
UbiOps
Weights & Biases
Common Industry Use Cases
arize.com
Product Release Notes
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GitBook
Azure & Databricks
Tutorial with Azure & Databricks
Tutorial Overview
This tutorial walks through
Using Arize in a Databricks workflow
Deploying the built model to a Azure Workspace
Google Colaboratory
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Last modified
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