Wallaroo Labs raises $25M Series A from Microsoft’s M12 to help with MLOps from cloud-to-edge
New York-based AI model management platform, Wallaroo (formerly known as Sendence) raised $25M in a Series A round led by Microsoft’s venture arm, M12. The involvement of M12 is important not only for the cash, but also for access to Microsoft’s M12 global network, the company noted. Wallaroo’s AI/ML management platform can integrate data from a wide array of sources, making it relevant to future enterprise plans to incorporate data from more sources at the network edge.
Wallaroo started with the aim of democratizing data and helping companies to adopt AI. As many large organizations are finding out, there is a gap between expecting financial gains from AI use and reality. On the one hand, research from McKinsey found that 56 percent of all respondents to its 2021 Global Insights Survey adoption of AI in at least one function, up from 50 percent in 2020. On the other hand, estimates from Gartner suggest that 85% of AI projects fail due to data and algorithm errors.
While working at Merril Lynch, founder and CEO Vid Jain realized the need for an organized AI platform that can efficiently work with data and help companies to tackle the bottlenecks in ML deployment. Jain said that the existing methodologies can be difficult to integrate with the ML ecosystem, which affects the scalability due to large costs. However, with the introduction of in-house technology, Wallaroo proposes a four-component strategy: MLOps, data processing engine, data connectors and audit/ performance metric.
Helping the data scientists and engineers at large organizations, Wallaroo’s model attempts to solve the challenges faced in deploying machine learning models into production for an impactful result. Wallaroo’s approach can run multiple machine learning models (busy with data processing) on shared infrastructure while minimizing the overhead. Easy integration and ML deployment with fast compute efficiency and advanced model analysis makes it an all-in-one AI management platform, according to executives.
“Machine learning is hard, and the last mile of ML – getting it deployed and delivering value – is really hard. 85 percent of enterprise initiatives fail at it,” said Jain. “We purpose-built Wallaroo to solve these last mile challenges. As more companies look to operationalize ML for the first time, and others grow their current ML investments to become a fully mature capability at scale, conquering ML’s last mile will quickly become their top priority. This new funding gives us the resources to help them deliver on the promise of AI,” he noted.
Some of the use cases where Wallaroo has gained customers include security, fraud, marketing, NLP, recommendations, computer vision and others. Enabling detection of model underperformance, system bottlenecks and data issues help Wallaroo offering over a claimed 80% savings on compute costs.
Work in progress
Above and beyond Wallaroo’s machine learning innovation for large organizations, the company plans to release a free community edition for independent data scientists and maker space to try out the AI management platform. These are strategically sound decisions that are primarily aimed at increasing customer feedback and serving partners with effective upgrades.
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Article Topics
AI/ML | edge data | Microsoft | MLOps | venture capital | Wallaroo Labs
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