Logging, monitoring and observability built-in

Logging. Testing. Monitoring. Observability.  Everything you need to know about your ML models in production.
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Machine learning teams deploying with Modelbit

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

Quickly diagnose problems in your models.

When you deploy with Modelbit you can easily access logs for each model deployment to diagnose any issues and identify the next steps to fix them.

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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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