All the bells and whistles

Custom Python environments. Highly available feature stores. Logging. Testing. Monitoring. Observability.  Everything you need to know your model is in good hands.
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Snowflake code calling a Modelbit model

As many custom Python environments as you need

Pick your model's Python version. Supply a list of Python packages and versions. For more complex models, supply a requirements.txt. If it installs with pip, your model can use it in production.

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Logging and observability built-in.

Track model load and model drift with Modelbit's built-in tools. Watch your model's logs in real time. And sync all your logs to your warehouse and your logs analysis tool of choice.

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

Unit tests for safe, trustworthy model deployment

Ensure that model deployments are safe with unit tests that automatically run whenever your model is deployed.

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Data from your database. Replicated in your feature store.

Replicate data directly from your data warehouse into Modelbit's fast, highly-available feature stores. Feature stores are available on-demand for models running in production.

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Sample data rows and results metadata from a Modelbit dataset

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