Modelbit
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Modelbit deploys and manages machine learning models efficiently with scalable, secure, and easy-to-use infrastructure.
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:
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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.
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Developer Workflow:
- Utilize
git pushto deploy models to isolated containers under your control. - Easily deploy your model by calling
mb.deployfrom your notebook with no framework requirements.
- Utilize
-
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.
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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.
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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.
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Deployment Flexibility:
- Deploy models in your private cloud for convenience and security or use Modelbit's managed cloud with the latest hardware configurations.
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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.
Modelbit FAQs
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What types of models can be deployed with Modelbit?
- Modelbit allows the deployment of any open-source or custom ML model, including computer vision models, open-source LLMs, and fine-tuned multimodal models.
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How does Modelbit streamline the deployment process?
- With Modelbit, developers can deploy their models using git push to fully isolated containers with full environment control.
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What benefits does Modelbit offer for managing ML models?
- Modelbit provides built-in MLOps tools and integrations for logging, monitoring, alerting, and overall management of ML models in production.
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Can Modelbit integrate with version control systems?
- Yes, Modelbit is backed by git repositories, supporting platforms like GitHub, GitLab, or custom repositories, enabling code review, CI/CD pipelines, PRs, and merge requests.
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How does Modelbit handle scaling and infrastructure requirements?
- Modelbit offers on-demand compute that automatically scales, allowing deployment in the cloud or VPC environments with next-gen infrastructure.
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What deployment options are available with Modelbit?
- Modelbit allows deploying models into data warehouses for easy inference, and models can also become REST APIs in the cloud for versatile usage across websites, mobile apps, and IT applications.
Get started with Modelbit
Modelbit reviews
Luca Sartori
January 31, 2025
What do you like most about using Modelbit?
The deployment speed is unmatched compared to other platforms we tried.
What do you dislike most about using Modelbit?
I encountered some bugs during the initial setup, but they were quickly resolved.
What problems does Modelbit help you solve, and how does this benefit you?
It allows for rapid iteration on our models, which is essential in our fast-paced industry.
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Oleg Petrov
February 16, 2025
What do you like most about using Modelbit?
The reliability and performance during high loads are exceptional, giving us confidence in our deployments.
What do you dislike most about using Modelbit?
Some advanced features are not as user-friendly as I'd hoped, but overall, it's a strong tool.
What problems does Modelbit help you solve, and how does this benefit you?
It helps us maintain high availability and performance, essential for our application's success.
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Aisha Mohamed
March 1, 2025
What do you like most about using Modelbit?
The ease of integration with existing systems is impressive; it saves us a lot of time.
What do you dislike most about using Modelbit?
The interface could use a refresh to make it more modern and user-friendly.
What problems does Modelbit help you solve, and how does this benefit you?
It allows us to deploy models rapidly, which is crucial for our fast-paced business environment.
How would you rate Modelbit?
What’s your thought?
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