Integrations: ML
Data Ingestion Integrations
| Files | Tables | SDK |
|---|---|---|
Monitoring Integrations
SlackOpsGeniePagerDutyAirflow RetrainAmazon EventBridge RetrainMLOps Partner Integrations
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 | ||
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 | ||
Ray Serve (Anyscale) | Arize can be easily integrated with Ray Serve with at single entry point during | ||
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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