Modelbit

What is Modelbit?

Modelbit is a platform designed to streamline machine learning workflows for developers. It allows users to deploy and iterate on models quickly with git-based deployment workflows. The platform offers fast and scalable infrastructure for running batch or real-time inference with on-demand compute that automatically scales up and down. Modelbit can be deployed to a secure cloud or to a user's own environment, providing flexibility and security. Trusted by ML leaders, Modelbit is known for its performance and ease of use in deploying and managing machine learning models efficiently.

Who created Modelbit?

Modelbit was founded by Harry Glaser, the Co-Founder & CEO, and Tom O'Neill, the Co-Founder & CTO. Harry previously served as co-founder and CEO of Periscope Data, with experience at Google and a BS in Computer Science. Tom was also a co-founder and CTO at Periscope Data, with a background at Bing search, Microsoft, and a BS in Computer Science. The company is based in San Francisco, California.

What is Modelbit used for?

• Deploy any open-source or custom ML model
• Developer workflow - git push to deploy models to fully isolated containers
• Run on next-gen infrastructure with on-demand compute that automatically scales
• Integrate ML with Git - everything backed by git repo
• Manage models with built-in MLOps tools and integrations
• Deploy models to private cloud or managed cloud with autoscaling compute
• Run models as REST APIs in the cloud
• Deploy models directly into data warehouse for easy inference
• Connect to favorite warehouse, feature store, experiment tracker, etc., with integrations
• Manage ML models in production with tools and integrations
• Build models with any technology
• Developer workflow - git push to deploy models to isolated containers
• Run on next-gen infrastructure with on-demand compute
• Integrate ML with Git - backed by git repo
• Deploy models anywhere - on autoscaling compute
• Deploy models into the warehouse for easy model inference
• Convert models into REST APIs for cloud integration
• Run ML models on fast, safe, and secure managed cloud
• Integrate with favorite ML tools and stack

Who is Modelbit for?

• Software Engineer
• Data Scientist
• Machine learning engineer
• Staff Software Engineer
• Co-Founder & CTO

How to use Modelbit?

To use Modelbit, follow these steps:

1. Build Models with Any Technology:

   • Deploy any open-source or custom ML model, including computer vision models built with PyTorch or fine-tuned multimodal models.
   • Modelbit can assist in deploying various models in minutes.

2. Developer Workflow:

   • Utilize git push to deploy models to isolated containers under your control.
   • Easily deploy your model by calling mb.deploy from your notebook with no framework requirements.

3. Deploy and Integrate:

   • Models can be deployed directly into your data warehouse for easy inference via SQL functions.
   • Modelbit models are transformed into REST APIs for use in websites, mobile apps, and IT applications, all backed by your Git repository.

4. Run on Next-Gen Infrastructure:

   • Access on-demand compute that scales automatically, either through the provided cloud service or by deploying Modelbit into your VPC.

5. Manage Models Professionally:

   • Benefit from built-in MLOps tools for logging, monitoring, alerting, and other production management tasks.
   • Integration with popular ML tools like Weights & Biases for an enhanced workflow.

6. Deployment Flexibility:

   • Deploy models in your private cloud for convenience and security or use Modelbit's managed cloud with the latest hardware configurations.

7. Enterprise Readiness:

   • Choose between different pricing plans based on your team's needs, ranging from On-Demand for small teams to Enterprise for custom requirements.
   • Get access to unlimited compute, deployment options, data integrations, and other advanced features based on your plan.

These steps outline the process of using Modelbit, covering model building, deployment, integration, infrastructure management, and enterprise-ready features.