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Examples

Example tutorials of how to use and troubleshoot with Arize.
Access tutorials of what's possible with Arize below:

Model Type Examples

Your model type determines which performance metrics are available to you. Learn more about model types here.
Model Type
Pandas Batch
Python Single Record
Parquet
CSV
Binary Classification (Only Classification Metrics)
Download File *Open Parquet Reader Here
Binary Classification (Classification, AUC/Log Loss Metrics)
Download File *Open Parquet Reader Here
Binary Classification (Classification, AUC/Log Loss, Regression)
Download File *Open Parquet Reader Here
Multiclass Classification (Only Classification Metrics)
Download File *Open Parquet Reader Here
Multiclass Classification (Classification, AUC/Log Loss Metrics)
Download File *Open Parquet Reader Here
Regression
Download File *Open Parquet Reader Here
Timeseries Forecasting
Download File *Open Parquet Reader Here
Ranking with Relevance Score
Download File *Open Parquet Reader Here
Ranking with Single Label
Download File *Open Parquet Reader Here
Ranking with Multiple Labels
Download File *Open Parquet Reader Here
NLP Classification
NLP Named Entity Recognition (NER)
CV Classification

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
Link
Amazon Web Services
Link
Azure File Import
Link

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 Platform
Description
Example Integration
Blog
Algorithmia
MLOps platform with APIs to serve, host and manages models
Blog
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.
Blog
Google Cloud ML (Vertex AI)
Integrate Arize with Vertex AI
Available on Request
Blog
Hugging Face
Use Arize to monitor embeddings generated from Hugging Face NLP or Transformer models
Blog
Kafka
Use Arize Pandas SDK to consumes micro-batches of predictions
Blog
MLFlow
Integrating Arize and MLflow to track the model across experimentation and deployment
Neptune
Integrate Arize on models built using Neptune
Blog
OpenAI
Paperspace
Integrate Arize on models built using Paperspace
Blog
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
Blog
Sagemaker
Spell
Combine Spell model servers with Arize model observability
Blog
UbiOps
Arize platform can easily integrate with UbiOps to enable model observability, explainability, and monitoring.
Blog
Weights & Biases
Integrating Arize and W&B to track the model across experimentation and deployment

Common Industry Use Cases