Examples
Example tutorials of how to use and troubleshoot with Arize.
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Example tutorials of how to use and troubleshoot with Arize.
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Access tutorials of what's possible with Arize below:
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
Binary Classification (Only Classification Metrics)
Binary Classification (Classification, AUC/Log Loss Metrics)
Binary Classification (Classification, AUC/Log Loss, Regression)
Multi-class Classification (Single-Label)
Multi-class Classification (Multi-Label)
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) - Text Summarization
Large Language Models (LLMs) - Search and Retrieval
SHAP: Guide to Getting Started
SHAP: Neural Network on Tabular Data
Surrogate Model Explainability
One Hot Encoding Decomposition
Google Cloud Services
Amazon Web Services
Azure File Import
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!
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
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Examples for logging explainability metrics. Click for more information on how to log feature importance and use explainability.