Train and Deploy ML Models on GPUs

Modelbit lets you train custom ML models with on-demand GPUs and deploy them to production environments with REST APIs.
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Machine learning teams deploying with Modelbit

Go from idea to inference in the tools you love.

Train and deploy any custom machine learning model

Modelbit works with the latest and greatest, from Segment-Anything to OWL-ViT; from LLaMa to GPT; and of course all your custom models built in any technology from Tensorflow to PyTorch.

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One line of code

Deploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.

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Deploy into Warehouse

Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!

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From Python to REST

Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.

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Backed by git

Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.

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

Isolated containers with on-demand GPUs

Each model is deployed to a fully custom, isolated Docker container, complete with load balancing, logging and disaster recovery. ML models deployed with Modelbit can be called as a REST endpoint directly from your product.

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Deploy to our cloud or to yours

Use Modelbit to deploy ML models into your cloud for maximum convenience paired with maximum security. Reach out to us request access.

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

Backed by your git repo

Modelbit 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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Deploy custom ML models built in any framework

Trusted by leading machine learning teams

Integrates with your modern data stack

From Python to production. No ML Engineers required.

Ready to see how Modelbit can help you deploy ML into production?