DataStax, the real-time AI company, announced it has acquired Kaskada, a machine learning (ML) company that first solved managing, storing and accessing time-based data to train behavioral ML models and deliver the instant, actionable insights that fuel artificial intelligence (AI). Both DataStax and Kaskada have a track record of contributing to open source communities. Datastax will open source the core Kaskada technology initially, and it plans to offer a new machine learning cloud service later this year.
Most machine learning initiatives don’t deliver the results that businesses need because the process is manual, complex and frustrating. Compounding this problem, many models underperform because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax’s portfolio of cloud services—which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra® and event streaming with Astra Streaming— will give organizations a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders such as Netflix and Uber.
“Businesses must operate in real time, using data to power operations and fuel instant, informed decisions and actions,” said Chet Kapoor, DataStax chairman and CEO. “DataStax has many customers already using real-time data, and with Kaskada as part of our services portfolio, we can give them the opportunity to use that data to create powerful experiences for their customers with real-time AI. It’s an exciting time for DataStax, and we have a clear new mandate: real-time AI for everyone.”
“Many companies struggle to see success with their big data projects because they don’t have the luxury of large ML and data engineering organizations–the cost is large and the time to impact is long,” said Davor Bonaci, Kaskada CEO. “We’re thrilled to join forces with DataStax to enable the real-time AI stack that just works, fueled with data from Astra DB.”
AI at Scale: Game-changing potential, but hard to achieve
According to Gartner, “By 2027, over 90% of new software applications that are developed in the business will contain ML models or services as enterprises utilize the massive amounts of data available to the business. These models will add data-driven intelligence to applications by integrating models that deliver next best actions, forecasts, scoring, risk assessment and many other attributes for both customer and employee transactions.1”
Yet many organizations have found it challenging to integrate this intelligence into their operational applications.
Matt Aslett, vice president and research director at Ventana Research noted: “The emergence of intelligent applications infused with personalization and artificial intelligence impacts requirements for operational data platforms to support real-time analytic functionality. The need for real-time interactivity means that these applications cannot be served by traditional processes that rely on the batch extraction, transformation and loading of data from operational data platforms into analytic data platforms for analysis. Instead, they rely on analysis of data in the operational data platform to accelerate decision-making or improve customer experience. High costs, complexity and scaling issues have been roadblocks to many organizations in achieving dynamic, real-time intelligence in their operational platforms.”
The Kaskada technology is designed to process massive amounts of event data as streams or stored in databases and its unique time-based capabilities create and update features for ML models based on sequences of events, or over time. It enables customers to adapt to rapidly evolving content and asynchronously creates features, allowing applications to use millions of predictions based on unique contexts.
“In e-commerce, you must be able to instantaneously act on insights to provide customers with the most impactful experiences; and that requires the application of machine learning on real-time transactions,” Martin Brodbeck, CTO at Priceline. “We have millions of customers using our website and mobile apps at any given moment and Astra DB is a powerful component of the Priceline data infrastructure. Our machine learning algorithms use massive data troves to provide valuable customer insights, greater personalization, and better travel recommendations, fueling our larger customer ecosystem.”
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