Machine learning is being incorporated into more and more technologies and industries. However, many companies find that deploying machine learning is expensive and that the learning curve is steep. BreezeML has developed products, built on a common infrastructure backbone, that can be used to quickly deploy and manage any company’s ML jobs, making the process faster and more cost effective. 

Ravi Netravali, Assistant Professor of Computer Science at Princeton, is the co-founder and chief technology officer of BreezeML, a startup whose platform is making it easier for companies to deploy, run and manage machine learning processes in their own systems. In late November, the BreezeML team announced a seed round of $4 million led by BlueRun Ventures, with participation by UpHonest Capital, Embark Ventures and others.

The BreezeML founding team came together while Prof. Netravali was at UCLA doing research on large scale machine learning systems. He and his co-founders quickly recognized that managing infrastructure for machine learning workflows is difficult and expensive for companies. They also noticed a gap between the capabilities of machine learning and the ability of companies to effectively incorporate them into their business logic. 

BreezeML was founded to help companies close the gap between the capabilities of artificial intelligence and machine learning and their implementation in business processes. “While AI is reshaping virtually all aspects of our daily lives, it costs a lot of money and effort to get it right and to get it functioning properly,” said Netravali. “We’ve built affordability into BreezeML as a first-class objective. We allow companies to focus on what they do best, while BreezeML’s technology works under the hood, making it easier for them to take advantage of all that ML/AI can bring to them.”

The BreezeML team, in their funding announcement, emphasizes their goal to help organizations “large and small, through the entire life-cycle of deploying and managing ML training and inference jobs.” BreezeML will “operate under the hood, alongside orchestration frameworks like Kubernetes, and leave the core logic of applications to the application developers,” wrote Netravali. This allows developers to focus on their unique company offerings, while giving them a high quality platform that takes care of all of the infrastructural details.

With the proliferation of AI and ML into various industries, the BreezeML team will attempt to mediate “the high infrastructure costs unaffordable to a large number of small and medium-sized companies,” commented Jimmy Shi, venture partner at BRV Aster. The democratization of AI through BreezeML will make “ML productization truly an easy and breezy experience.” 

With the recent close of their seed raise, the BreezeML team is rapidly building a cloud engineering team and is seeking strong cloud infrastructure engineers with design, architecture and implementation experience. More information can be found at breezeml.ai/career.