Train your ML models with on-demand GPUs

Call modelbit.add_job() in any Python environment to train your ML models with on-demand compute.
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Machine learning teams deploying with Modelbit

Snowflake code calling a Modelbit model

Create ML training jobs in any environment

Call modelbit.add_job() in any data science notebook or Python environment to train your ML models with on-demand GPUs.

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Retrain your ML models on a regular basis

Modelbit makes it easy to schedule ML model retraining jobs on a regular basis. Jobs run in the same environment as the deployment, using the same Python and system packages.

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

Create ML model training jobs with git

Training jobs can be customized to deploy new versions of the model, run on a schedule, as well as refresh the datasets they depend on.

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Track and visualize training progress in Modelbit or your favorite tools

You can track training progress in Modelbit, or send your training job's logs to your favorite ML tools like Weights & Biases or neptune.ai.

Track in neptune.aiTrack in Weights & Biases
Sample data rows and results metadata from a Modelbit dataset

Explore Modelbit

Infer everywhere

Call your model from Snowflake, Redshift, dbt and REST.

Backed by your git repo

Version control, CI/CD, code review and more. All with your own git tools.

All the bells and whistles

Logging, monitoring, observability and more.

Ready to see an ML platform you will love?

Get a demo and learn how ML teams are deploying and managing ML models with Modelbit.
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