Deploy ML models to Snowflake, Redshift, dbt and REST
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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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Deploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.
Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!
Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.
Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.
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!
Learn MoreModelbit 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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Learn how Inventa reduced predicted shipping times by 66%, thus increasing shopping cart conversions and eliminating a top customer complaint. Learn more.
Bambee deployed a machine learning model that can score leads in batch in their Snowflake warehouse as well as online with a REST API.
