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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
CSV
Parquet
Binary Classification (Only Classification Metrics)
​Colab Link​
​Colab Link​
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​Download​ File *Open Parquet Reader Here​
Binary Classification (Classification, AUC/Log Loss Metrics)
​Colab Link​
​Colab Link​
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​Download File *Open Parquet Reader Here​
Binary Classification (Classification, AUC/Log Loss, Regression)
​Colab Link​
​Colab Link​
​Download File​
​Download File *Open Parquet Reader Here​
Multiclass Classification (Only Classification Metrics)
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
Multiclass Classification (Classification, AUC/Log Loss Metrics)
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
Regression
​Colab Link​
​Colab Link​
​Download File​
​Download ​File *Open Parquet Reader Here​
Timeseries Forecasting
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
Ranking with Relevance Score
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
Ranking with Single Label
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
Ranking with Multiple Labels
​Colab Link​
​Colab Link​
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​Download ​File *Open Parquet Reader Here​
NLP Classification
​Colab Link​
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NLP Named Entity Recognition (NER)
​Colab Link​
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CV Classification
​Colab Link​
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Tabular Classification w/ Embeddings
​Colab Link​
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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
​Colab Link​
SHAP: Neural Network on Tabular Data
​Colab Link​
Surrogate Model Explainability
​Colab Link​
One Hot Encoding Decomposition
​Colab Link​

Cloud Storage Examples

Google Cloud Services
​Link​
Amazon Web Services
​Link​
Azure File Import
​Link​

Benchmark Test

Sending 10 Million Inferences to Arize in 90 Seconds
​Colab Link​

Logging Predictions, Actuals, SHAP Values

Tutorials on how to log predictions, actuals, and feature importance.
Logging Predictions Only
​Colab Link​
Logging Predictions First, Then Logging Delayed Actuals
​Colab LInk​
Logging Predictions First, Then Logging SHAPs After
​Colab Link​
Logging Predictions and Actuals Together
​Colab Link​
Logging Predictions and SHAP Together
​Colab Link​
Logging Predictions, Actuals, and SHAP Together
​Colab Link​
Logging PySpark DataFrames
​Colab Link​

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
​Colab Link​
​Blog​
Azure ML & Databricks
Using Arize in an Azure ML Databricks workflow
​Colab Link​
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Bento ML
Use Bento’s ML service platform to turn ML models into production-worthy prediction services
​Tutorial Blog​
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CML
Integrate Arize into the CI/CD workflow - Run checks on every new model version
​Example here​
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Deepnote
Deepnote is a Data Science Collaboration Platform
​Deepnote Link​
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Feast
Monitor & Troubleshoot any data inconsistency issue with feature stores Arize.
​Colab Link​
​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
​Example here​
​Blog​
MLFlow
Integrating Arize and MLflow to track the model across experimentation and deployment
​Colab Link​
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Neptune
Integrate Arize on models built using Neptune
​Colab Link​
​Blog​
OpenAI
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​Colab Link (NLP)​
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Paperspace
Integrate Arize on models built using Paperspace
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​Blog​
PySpark
To log Spark DataFrames, which have rdds as their underlying structure, we will use mapInPandas to log them to arize.
​Colab Link​
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Ray Serve (Anyscale)
Arize can be easily integrated with Ray Serve with at single entry point during ray.serve.deployment
​Overview
​Blog​
Sagemaker
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​Batch Real-Time​
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Spell
Combine Spell model servers with Arize model observability
​Overview Colab Link​
​Blog​
UbiOps
Arize platform can easily integrate with UbiOps to enable model observability, explainability, and monitoring.
​Colab Link​
​Blog​
Weights & Biases
Integrating Arize and W&B to track the model across experimentation and deployment
​Colab Link​
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Common Industry Use Cases