Wallaroo, an MLOps platform for enterprises, raises $25M Posted on : Feb 10 - 2022

Enterprises are increasingly looking to AI for opportunities to boost revenue as their operations move online. According to a 2021 PricewaterhouseCoopers survey, a quarter of companies report widespread adoption of AI in their organizations — up from 18% in 2020. But AI projects are at risk of stalling due to the many roadblocks businesses encounter on the pathway to implementation. In a Gartner report, analysts estimate that 85% of AI projects will deliver erroneous outcomes — whether due to bias in the data and algorithms or the teams managing them.

An emerging discipline called machine learning operations, or MLops, aims to prevent these failures by combining machine learning, devops, and data engineering to facilitate the deployment and maintenance of AI models. MLops is a fast-expanding category of companies, anticipated to generate as much as $4 billion in revenue by 2025. Among the players is Wallaroo, which offers a New York-based AI model management platform that can plug into existing systems. Underlining the segment’s growth, Wallaroo today announced that it raised $25 million in a series A round led by M12, Microsoft’s venture arm, with participation from Boldstart Ventures, Contour Venture Partners, Eniac Ventures, and Greycroft, bringing the company’s total raised to $30 million.

Bringing AI to the enterprise

Founded in 2014 as Sendence by CEO Vid Jain, Wallaroo offers services designed to help customers deploy and scale AI their investments. Jain created the concept while working at Merril Lynch, where he realized that the company could derive more value from AI by adopting an improved “last-mile” deployment strategy.

“Data is everywhere, and enterprises across every sector are turning to machine learning to use that data to become more competitive, agile, and profitable. These enterprises, however, are confronting a fundamental roadblock: how to put their machine learning models into production so that those models actually have an impact on the bottom line,” Jain told VentureBeat via email. “Operationalizing data applications at scale is really hard. Existing approaches — whether containerization, cobbling together various existing technologies, or customizing an analytics workhorse like Apache Spark — are cumbersome, limited in scope, expensive at scale and prone to failure.”

The Wallaroo platform is built on four components: MLops, a distributed processing engine, data connectors, and audit and performance metrics. It’s Jain’s assertion that by combining these, Wallaroo can run multiple models on shared infrastructure without adding significant overhead to the models’ compute times. View more