Outerbounds provides a platform for engineering production-grade AI products by integrating data, models, and agents with software discipline. It enables rapid development and evaluation of AI systems using Metaflow, supporting CI/CD workflows for models and code. The platform securely deploys these systems within the customer's cloud environment, offering access to top-tier GPU providers while optimizing compute costs.
Funding
$24.3M 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
Developing, scaling, and deploying machine learning (ML) and artificial intelligence (AI) systems requires significant infrastructure management, often burdening data scientists and ML engineers and slowing down the development lifecycle. Organizations struggle to efficiently utilize diverse compute resources across multiple cloud environments while maintaining security and cost control.
Solution
Outerbounds provides a human-centric platform that simplifies the development, scaling, and deployment of production-ready AI/ML systems. The platform leverages the open-source Metaflow framework to offer a streamlined experience, allowing data scientists and ML engineers to focus on model development rather than infrastructure complexities. Outerbounds enables organizations to unify compute resources across AWS, Azure, GCP, on-prem clusters, and specialized providers, optimizing cost efficiency and resource utilization. The platform provides built-in integrations, security features, and monitoring tools, ensuring robust and reliable operation of ML systems.
Target Audience
Outerbounds targets data scientists, machine learning engineers, and platform engineers in organizations across various industries, including finance, healthcare, logistics, and e-commerce, who seek to streamline their AI/ML development and deployment processes.
Features
- Cloud workstations with integrated VSCode or notebooks for a seamless development experience.
- Support for state-of-the-art LLMs and GenAI models, privately running in the user's cloud account.
- Automated Docker image creation for simplified cloud execution.
- Unified compute pools across multiple cloud providers (AWS, Azure, GCP) and on-prem clusters.
- Built-in cost optimization views for attributing and reducing cloud compute costs.
- Event-driven workflows for ETL, continuous training, batch inference, and data processing.
- Integration with data warehouses, microservices, and third-party tools with granular RBAC.
- Automatic versioning and tracking of code, data, and models.