Examples

Example tutorials of how to use and troubleshoot with Arize.

Access tutorials of what's possible with Arize below:

LLM Examples

LLM Tracing

Prompt Template Iteration

Search and Retrieval with Llamaindex

Langchain Integration

Text Summarization

Model Type Examples

Model TypePandas BatchPython Single RecordCSVParquet

Binary Classification (Only Classification Metrics)

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Binary Classification (Classification, AUC/Log Loss Metrics)

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Binary Classification (Classification, AUC/Log Loss, Regression)

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Multi-class Classification (Single-Label)

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Multi-class Classification (Multi-Label)

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Regression

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Timeseries Forecasting

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Ranking with Relevance Score

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Ranking with Single Label

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Ranking with Multiple Labels

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NLP Classification

NLP Named Entity Recognition (NER)

CV Classification

Tabular Classification w/ Embeddings

Object Detection

Large Language Models (LLMs) - Text Summarization

Large Language Models (LLMs) - Search and Retrieval

Explainability Tutorials

Examples for logging explainability metrics. Click here for more information on how to log feature importance and use explainability.

SHAP: Guide to Getting Started

SHAP: Neural Network on Tabular Data

Surrogate Model Explainability

One Hot Encoding Decomposition

Cloud Storage Examples

Google Cloud Services

Amazon Web Services

Azure File Import

Benchmark Test

Sending 10 Million Inferences to Arize in 90 Seconds

Logging Predictions, Actuals, SHAP Values

Tutorials on how to log predictions, actuals, and feature importance.

Logging Predictions Only

Logging Predictions First, Then Logging Delayed Actuals

Logging Predictions First, Then Logging SHAPs After

Logging Predictions and Actuals Together

Logging Predictions and SHAP Together

Logging Predictions, Actuals, and SHAP Together

Logging PySpark DataFrames

Example Integrations with Common ML/Data Platforms

Arize integrates with platforms across the MLOps toolchain. Don't see a platform you use? Reach out to add yours or ask our team to help!

ML PlatformDescriptionExample IntegrationBlog

Algorithmia

MLOps platform with APIs to serve, host and manages models

Anyscale

Integration tutorial for Anyscale's LLM Endpoints offering

Azure ML & Databricks

Using Arize in an Azure ML Databricks workflow

Bento ML

Use Bento’s ML service platform to turn ML models into production-worthy prediction services

CML

Integrate Arize into the CI/CD workflow - Run checks on every new model version

Deepnote

Deepnote is a Data Science Collaboration Platform

Feast

Monitor & Troubleshoot any data inconsistency issue with feature stores Arize.

Google Cloud ML (Vertex AI)

Integrate Arize with Vertex AI

Available on Request

Hugging Face

Use Arize to monitor embeddings generated from Hugging Face NLP or Transformer models

Kafka

Use Arize Pandas SDK to consumes micro-batches of predictions

Langchain

Effectively monitor the performance of your LLM agents

MLFlow

Integrating Arize and MLflow to track the model across experimentation and deployment

Neptune

Integrate Arize on models built using Neptune

OpenAI

Build unstructured models with OpenAI

Paperspace

Integrate Arize on models built using Paperspace

PySpark

To log Spark DataFrames, which have rdds as their underlying structure, we will use mapInPandas to log them to arize.

Ray Serve (Anyscale)

Arize can be easily integrated with Ray Serve with at single entry point during ray.serve.deployment

Sagemaker

Spell

Combine Spell model servers with Arize model observability

UbiOps

Arize platform can easily integrate with UbiOps to enable model observability, explainability, and monitoring.

Weights & Biases

Integrating Arize and W&B to track the model across experimentation and deployment

Common Industry Use Cases

pageCommon Industry Use Cases

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