Integrations: ML
Last updated
Last updated
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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
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