OctoAI Stock

octo.aiEnterprise Software / Data IntelligenceFounded: 2019Funding to Date: $132.6MM

OctoML is a machine learning deployment platform with a mission to make machine learning more accessible. Its technology generates production-ready software functions that easily integrate with an organization's existing application stacks and DevOps workflows. The company's technology offers a managed service using machine learning to automate machine language code generation and optimization in multi-cloud environments ensuring that the models keep on running at peak efficiency, providing businesses with secure deployments of deep learning models.

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Team

Management Team

Tianqi Chen
Co-Founder & Chief Technologist
David Messina
Chief Marketing Officer
Luis Ceze Ph.D
Co-Founder, Chief Executive Officer & Board Member
Jared Roesch
Co-Founder & Chief Technology Officer
Jason Knight Ph.D
Co-Founder & Vice President, Machine Learning
Tony Tzeng
Chief Product Officer
Thierry Moreau Ph.D
Co-Founder & Head of DevRel
Itay Neeman
Vice President of Engineering
Anna Connolly
Vice President of Customer Success and Experience
Amanda Robles
Vice President of People and Operations

Board Members

Matthew McIlwain
Madrona Venture Group
Michael Dauber
Amplify Partners

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Seattle-based OctoAI has a new offering called OctoStack, designed to help those in the enterprise deploy private generative AI models. Companies can use this “turn-key production platform” in a virtual private cloud or on-premises and will have access to highly optimized inference, model customization and asset management. In doing so, OctoAI wants to give companies the freedom to build and run gen AI applications in the way they see fit.
OctoML is now OctoAI. The Seattle startup this week revealed the slight but notable tweak to its name. The change reflects a growing product suite, according to CEO and co-founder Luis Ceze.
When OctoML launched in 2019, its primary focus was optimizing machine learning (ML) models. Since then, the company has added features that make it easier to deploy ML models (and raised $132 million). Today, the company is launching the latest iteration of its service — and while it’s not quite a pivot, it does shift the company’s emphasis from optimizing models to helping businesses use existing open-source models and fine-tune them with their own data or use the service to host their own custom models.
With big disruptors like ChatGPT and Bing giving the world a glimpse of what’s possible with artificial intelligence at full strength, companies globally are joining the trend of becoming artificial intelligence-native and operationalizing machine learning models.
Updated on: Jul 26, 2024

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