Tutorials
Example tutorials of how to use and troubleshoot with Arize.
Access tutorials of what's possible with Arize below:
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) | | |||
Binary Classification (Classification, AUC/Log Loss Metrics) | | |||
Binary Classification (Classification, AUC/Log Loss, Regression) | ||||
Multiclass Classification (Only Classification Metrics) | | |||
Multiclass Classification (Classification, AUC/Log Loss Metrics) | | |||
Regression | ||||
Timeseries Forecasting | | |||
Ranking with Relevance Score | | |||
Ranking with Single Label | | |||
Ranking with Multiple Labels | | |||
NLP Classification | | | | |
NLP Named Entity Recognition (NER) | | | | |
CV Classification | | | | |
Tabular Classification w/ Embeddings | | | | |
Object Detection | | | | |
Large Language Models (LLMs) | | | |
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 |
Sending 10 Million Inferences to Arize in 90 Seconds |
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 |
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 | ||
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 | |
Last modified 4d ago