Machine learning deployment platform OctoML raises $85M | VentureBeat

Let the OSS Enterprise newsletter guide your open source journey! Sign up here.

OctoML, a platform that helps enterprises optimize and deploy machine learning (ML) models, has raised $85 million in a series C round of funding.

While countless companies are dabbling with ways to leverage AI to improve their businesses and bottom line, transitioning AI projects beyond the pilot stage and into real-world production scenarios comes is no easy feat. Indeed, in its State of AI in 2020 report, McKinsey found that just 16% of respondents from across industries had taken their deep learning beyond the pilot stage — and this, ultimately, is what OctoML is all about.

From pilot to production

Founded out of Seattle in 2019, OctoML helps companies deploy ML models through to production environments. It does this by automatically tailoring models to suit the target hardware platform, cloud provider, or edge device, with no manual rewriting or re-architecting required — in other words, it saves a significant amount of time and resources. The company has cemented official partnerships with major hardware firms such as AMD, Arm, and Qualcomm.

OctoML is built on the open source Apache TVM, which is a machine learning compiler framework for central processing units (CPUs), graphics processing units (GPUs), and machine learning accelerators — it enables ML engineers to run more efficient computations on any hardware. Perhaps most notably, OctoML was founded by the Apache TVM creators, which includes CEO Luis Ceze.

“The ever-growing ecosystem of ML hardware backends and diverse models are generating an insurmountable amount of manual work to optimize and fine-tune models before deployment,” Ceze noted in a press release. “This is resulting in skyrocketing costs, significant delays in time to production, and impeding new use cases in resource-constrained edge devices.”

In short, OctoML wants to help AI projects succeed by removing barriers and improving accessibility to a far broader spectrum of users. That might mean smaller engineering teams with fewer resources, or larger businesses such as Toyota that are looking to make better use of their existing resources.

Prior to now, OctoML had raised around $47 million, and with another $85 million in the bank from its lead investors Tiger Global Management, Addition, Madrona Venture Group, and Amplify Partners, the company said that it’s well-financed to expand its roster of partnerships across hardware vendors and cloud providers.

“Our ecosystem efforts are driven by our vision for the company, which is to make ML accessible to as many developers, anywhere and on any device,” Ceze explained.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access: