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 MoreWe 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 MoreWe 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 MoreWe 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 MoreJupyter Notebook. Colab. Hex. No matter which you choose, pip install modelbit and modelbit.deploy() works out of the box.
Learn MoreJupyter Notebook. Colab. Hex. No matter which you choose, pip install modelbit and modelbit.deploy() works out of the box.
Learn MoreDeploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.
Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!
Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.
Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.
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 MoreWhen 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.
Deploying your model is as simple as calling mb.deploy right from your notebook. No need for special frameworks or code rewrites.
Models are deployed directly into your data warehouse, where making a model inference is as easy as calling a SQL function!
Modelbit models become REST APIs in the cloud. Your team can call them from websites, mobile apps, and IT applications.
Modelbit is backed by your git repo, where data is secure and version controlled, and CI/CD runs when changes are deployed.
From model experiment trackers, hosted data science notebooks, to feature stores and Snowpark ML.
Modelbit integrates with your ML stack.
Use Modelbit to deploy ML models into your cloud for maximum convenience paired with maximum security. Reach out to us request access.
Request AccessModelbit 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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