Runhouse is a serverless machine learning platform that enables ML engineers and data scientists to define and execute training pipelines in standard Python across various compute environments, including Kubernetes and elastic compute. It eliminates the barriers between research and production by allowing seamless code deployment and debugging, enhancing development speed and operational efficiency.
Funding
$4.9M 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.
Founders
Product
Problem
Machine learning engineers and data scientists face challenges in deploying and managing training pipelines across diverse compute environments. Silos between research and production environments lead to slow deployment cycles and debugging complexities. Existing solutions often require significant code translation and infrastructure management overhead.
Solution
Runhouse is a serverless machine learning platform that enables the definition and execution of training pipelines in standard Python across various compute environments, including Kubernetes and elastic compute. It allows developers to dispatch execution to arbitrary compute, such as Kubernetes, elastic compute, and bare metal. The platform facilitates rapid code deployment and debugging, enhancing development speed and operational efficiency by eliminating the barriers between research and production. Runhouse allows users to manage their ML lifecycle using software development best practices on regular code, deploying with no extra translation.
Target Audience
The primary users are machine learning engineers, data scientists, and research teams looking to streamline the deployment and management of ML pipelines across diverse compute environments.
Features
- Define and dispatch training pipelines in standard Python.
- Supports various compute environments, including Kubernetes and elastic compute.
- Deploy code updates in less than 5 seconds with streaming logs for fast, iterative development.
- Built-in infrastructure observability, including automatic log persistence and resource utilization tracking.
- Integrates with existing ML pipelines, code, and development workflows.
- Provides a search and sharing interface to explore ML artifacts.
- Offers authentication and access control features for managing team access to services.