Deploy ML models to Snowflake, Redshift, dbt and REST

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A Jupyter notebook deploying a Python model to Modelbit
Deploy to the modern data stack. With your trusted data science tools.
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Deploy right from your notebook

Don't change your day-to-day! Works with Jupyter Notebooks and any other Python environment. Simply call modelbit.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production.

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One line of code

Deploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.

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Deploy into Warehouse

Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!

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From Python to REST

Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.

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Backed by git

Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.

Snowflake code calling a Modelbit model

Call your model from your warehouse. And from your product.

ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product!

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Backed by your git repo

Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and Merge Requests. Bring your whole git workflow to your Python ML models.

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Sample data rows and results metadata from a Modelbit dataset

Integrates with your modern data stack

From Python to production. No ML Engineers required.

Trusted by leading data teams

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