Modelbit works with Jupyter and other leading data science notebooks. Deploy your model right from the Notebook where you build and trained it. Send it to production with one line of code.
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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.
Once deployed with Modelbit, your model is callable in SQL from your data warehouse. Make predictions directly from dbt jobs.
Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.
Your code is only accessible with your API key. Models are version controlled so you always know what you're getting in production.
Python models deployed to Modelbit from your Notebook automatically become functions in your Snowflake or Redshift warehouse. There, they can be called from dbt jobs, when new rows are inserted, or in batch.
Try ModelbitDatasets are managed and governed in Modelbit, where your whole team can collaborate on them. Ensure that everyone is using the same version of the data. Pull Modelbit datasets down into notebooks as Pandas DataFrames.
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Harry contributes a bit of design, a bit of questionable JavaScript, and does most of the talking and emailing. He was previously co-founder and CEO of Periscope Data. Before that he had a stint in product at Google and got a BS in Computer Science.
Tom architects and builds Modelbit. He keeps a watchful eye on the constellation of ML models running in our cloud. Previously he was co-founder and CTO of Periscope Data. Before that he worked on Bing search at Microsoft, and got a BS in Computer Science.
