Examples
Example tutorials of how to use and troubleshoot with Arize.
Access tutorials of what's possible with Arize below:
LLM Examples
Tracing
LlamaIndex Tracing
Langchain Tracing
Logging data manually
Evaluation
Running Evals with LlamaIndex
Running Evals with Langchain
By Use Case
Prompt Template Iteration
Search and Retrieval with Llamaindex
Langchain Callback Integration
Text Summarization
Model Type Examples
Binary Classification (Only Classification Metrics)
Binary Classification (Classification, AUC/Log Loss Metrics)
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Binary Classification (Classification, AUC/Log Loss, Regression)
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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
SHAP: Neural Network on Tabular Data
Surrogate Model Explainability
One Hot Encoding Decomposition
Cloud Storage Examples
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!
Bento ML
Use Bento’s ML service platform to turn ML models into production-worthy prediction services
Hugging Face
Use Arize to monitor embeddings generated from Hugging Face NLP or Transformer models
MLFlow
Integrating Arize and MLflow to track the model across experimentation and deployment
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
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
Common Industry Use CasesLast updated