Modelbit is an infrastructure-as-code platform that enables machine learning engineers to deploy, manage, and scale ML models in production environments with a single git push command. It addresses the complexities of model deployment, including autoscaling, retraining, and drift detection, by allowing all configurations to be managed directly from the user's git repository.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
LPFounders
Product
Problem
Deploying and managing machine learning models in production environments involves complexities such as autoscaling, retraining, drift detection, and infrastructure management. Existing solutions often require extensive manual configuration and lack seamless integration with existing development workflows. This creates bottlenecks and slows down the deployment and iteration of ML models.
Solution
Modelbit is an infrastructure-as-code platform designed to streamline the deployment, management, and scaling of machine learning models in production. By integrating with a user's Git repository, Modelbit allows for all configurations to be managed as code, enabling automated deployments with a simple `git push` command. The platform handles autoscaling, retraining, drift detection, and shadow deployments, providing a comprehensive solution for the entire ML ops workflow. Each deployment is containerized and isolated, ensuring stability and preventing interference between different models.
Target Audience
Modelbit is designed for machine learning engineers and data science teams with serious production ML infrastructure needs, particularly those dealing with demanding workloads such as real-time threat detection, large-scale text understanding, and fraud detection.
Features
- Infrastructure-as-code approach for managing ML deployments directly from a Git repository
- Automated deployment pipeline triggered by `git push` commands
- Autoscaling capabilities to handle varying workloads
- Integrated drift detection to monitor model performance
- Support for shadow deployments to test new models in production
- Containerized and isolated deployments for enhanced stability
- High-level Python API for data scientists and ML researchers
- CLI and web application for managing staging and production environments
- REST API and Snowflake API for accessing deployed models