Modern MLOps in the Cloud

Run your model code on next-gen infrastructure with built-in tools for managing ML in production.

Deploy any ML model in seconds.
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Machine learning teams run on Modelbit

Launch your next ML project in minutes

Rapidly train and deploy custom and open-source ML models.
Build with any technology

Train and deploy any ML model

Computer vision models built with PyTorch. Open-source LLMs. Fraud detection. If you can run it in a notebook - Modelbit can deploy your model in seconds.

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Run on next-gen infrastructure

On-demand compute that automatically scales

We built a new compute framework that scales up and down as needed. Run on our compute, deploy to your VPC, or even push jobs to Snowpark ML.

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Run on next-gen infrastructure

On-demand compute that automatically scales

We built a new compute framework that scales up and down as needed. Run on our compute, deploy to your VPC, or even push jobs to Snowpark ML.

Learn More
Run on next-gen infrastructure

On-demand compute that automatically scales

We built a new compute framework that scales up and down as needed. Run on our compute, deploy to your VPC, or even push jobs to Snowpark ML.

Learn More
Manage your models like a pro

Built-in MLOps tools and integrations

Jupyter Notebook. Colab. Hex. No matter which you choose, pip install modelbit and modelbit.deploy() works out of the box.

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Trusted by ML leaders

Machine learning teams that want to move fast choose Modelbit.

How Modelbit Works

Deploy your ML models to REST endpoints from any Python environment.
1. Install

Install Modelbit in any Python environment

Jupyter Notebook. Colab. Hex. No matter which you choose, pip install modelbit and modelbit.deploy() works out of the box.

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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
2. Build

Build and train any custom machine learning model

Modelbit lets you quickly deploy the latest and greatest ML models, 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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3. Deploy

Deploy your model to a production environment behind a REST API

When you call modelbit.deploy() your 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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Python icon

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.

Data warehouse icon

Deploy into Warehouse

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

Code icon

From Python to REST

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

Lock icon

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

Built-in MLOps tools that help you scale

When you deploy models with Modelbit, you get all the tools and integrations you need to run ML in production.

Want to see Modelbit in action?

Watch the demo below to see us deploy a Segment Anything Model to production.

Deploy anywhere. Integrate everywhere.

Modelbit lets you deploy ML models to scalable and secure production environments.

Integrate with your favorite ML tools

From model experiment trackers, hosted data science notebooks, to feature stores and Snowpark ML.

Modelbit integrates with your ML stack.

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Deploy ML models 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

Everything 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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Integrates with your modern data stack

From Python to production. No ML Engineers required.

Get started with 50 free compute minutes.