Databricks, a data analytics platform with a strong focus on AI and machine learning (ML), recently raised $400 million in a series F funding round led by venture capital firm Andreessen Horowitz, which included Microsoft, BlackRock, and other investors. Databricks plans to use the money to hire more engineers to accelerate R&D.
Founded just six years ago by the original creators of Apache Spark, Databricks quickly grew from a project at the University of California, Berkeley, to a cloud-based “unified data analytics platform.” It is now valued at $6.2 billion.
Databricks provides a suite of tools for massive scale data engineering, enterprise analytics, data science, and ML, including automated cluster management, direct pipelines into analytical tools (Tableau, etc.), one-click access to preconfigured ML environments, virtual notebooks for real-time collaborative programming, and ML lifecycle tools. It can run on any cloud platform and has partnerships with Microsoft and Amazon Web Services (AWS).
Courtesy: Databricks Unified Data Analytics Platform.
Databrick’s customers include Hotels.com, Viacom, HP, Shell Energy, Showtime, Riot Games, Sanford Health, Expedia, Condé Nast, McGraw-Hill, Zeiss, Cisco, NBCUniversal, Overstock, Nielsen, HP, Dollar Shave Club, and more across a wide range of industries. Many of them use Databricks to do “boring AI” which nevertheless brings tremendous value. One retailer, for example, is saving $30 million annually by having figured out when to dim or turn off the lights in its frozen foods aisle section in the stores. Another customer saves $20 million per year by optimizing pick-up schedule for its carton packaging.
Databricks intends to use the new funds to double its engineering staff (from the current 400 staff) in the coming year to accelerate R&D and to finance its continued global expansion. So, what kind of new capabilities can we expect to see in the future?
This analyst hopes that some of the money will go to building out AI as a Service, and more specifically, AI Apps as a Service, providing pretrained ML applications which could be deployed by customers with minimal tuning or retraining, without needing to build and train ML models from scratch. Google, Amazon, Microsoft, and a slew of start-ups are already starting to provide such services. And we expect to see more, since many organizations do not have the necessary resources to build ML, nor can they acquire them easily.
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