New Feature - Deploy to Snowpark

Say Goodbye to SageMaker

Get everything you need to serve machine learning models in production.
-  Deploy any model to a REST API in minutes.
-  Automatically sync your model code with Git.
-  MLOps tools like alerting, logging, and monitoring.
-  Autoscaling compute, in your cloud or ours.
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Machine learning teams run on Modelbit

Ship better models, faster

Spend time building and shipping better ML models, not updating your infrastructure.

Deploy with Git or Python APIs

Deploy any ML model in minutes. Modelbit automatically detects your model's environment and dependencies, and deploys it to an isolated container behind inference endpoints.

Fast, scalable inference

Run batch or real time inference with serverless infrastructure that automatically scales on demand. Fast cold starts and fully configurable latency requirements.

Enterprise Readiness

Deploy to our secure cloud or to your own. Modelbit is backed by your Git repo, and built from the ground up to be fast, safe, and secure.

Trusted by ML leaders

Machine learning teams that move fast choose Modelbit.
“We retrain and redeploy thousands of models daily, so choosing the right partner for model deployment is critical. We initially investigated SageMaker, but Modelbit’s performance paired with its ease of use was a game-changer for us.” Read more.
Cody Greco
Co-Founder &CTO
"With Modelbit, we’ve been able to experiment with new models faster than ever before. Once a model is up, we’re able to make iterations in a manner of minutes. It feels a bit like magic every time I make a change to our model code, push it to Github, and see it live on Modelbit just seconds later."
Daniel Mewes
Staff Software Engineer
"Modelbit enabled us to easily deploy several large vision transformer-based models to environments with GPUs. Rapidly deploying and iterating on these models in Modelbit allowed us to build the next generation of our product in days instead of months."
Nick Pilkington
Co-Founder &CTO
Explore Case Studies

How Modelbit Works

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

Learn More
Sample data rows and results metadata from a Modelbit dataset
2. Deploy from anywhere

Deploy your models from any Python environment

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

Learn More
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.

2. Deploy from anywhere

Deploy your models from any Python environment

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

Learn More
Sample data rows and results metadata from a Modelbit dataset
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.

3. 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
Snowflake code calling a Modelbit model
4. Integrate your ML with Git

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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5. Manage your models like a pro

Built-in MLOps tools and integrations

Once your models are deployed you'll get logging, monitoring, alerting, and all the tools you need to manage ML in production. Modelbit also integrates with your favorite ML tools like Weights & Biases, and many more.

Learn More

Trusted by ML leaders

Machine learning teams that want to move fast choose Modelbit.
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.

Learn More
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.

Learn More
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.

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.

Request Access
Sample data rows and results metadata from a Modelbit dataset

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.

Learn More

Integrates with your modern data stack

From Python to production. No ML Engineers required.

Ready to deploy your ML model?

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