Deploy any ML model, from any Python environment

  • Deploy ML from the same tools you develop in.
  • Jupyter, Hex, Deepnote, VS Code, and more.
  • modelbit.deploy() works everywhere.
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Machine learning teams deploying with Modelbit

Snowflake code calling a Modelbit model

Deploy ML models from hosted data science notebooks

Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production.

Deploy From HexDeploy From Deepnote
Snowflake code calling a Modelbit model

Every ML model gets its own isolated container with on-demand GPUs

That means you can easily shadow deploy your ML models for safe, effective testing against live traffic with split and A/B tests.

When you're ready, call your model's REST API in your product.

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Deploy ML models from Jupyter in all its forms

Jupyter Notebook. Jupyter Lab. Colab. SageMaker notebooks. No matter which you choose, pip install modelbit and modelbit.deploy() works out of the box.

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Sample data rows and results metadata from a Modelbit dataset
Snowflake code calling a Modelbit model

Migrate your SageMaker models to Modelbit

Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built.

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Deploy with git

For models built outside of notebooks, deploy from the command line, your IDE, or your favorite git tool. Deploy to production with a simple git push.

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Sample data rows and results metadata from a Modelbit dataset

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Ready to deploy your ML model?

Get a demo and learn how ML teams are deploying and managing ML models with Modelbit.
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