According to a recent press release, “DataRobot, the leader in enterprise AI, has partnered with Kx to offer financial institutions and IoT-driven industries a comprehensive, scalable high-performance solution for applying AI to time series data. By integrating DataRobot’s Enterprise AI Platform with the Kx database platform kdb+—the world’s fastest in-memory time series database—the partnership, which was unveiled during the Kx Innovation Day at Aston Martin Red Bull Racing headquarters, allows consumers of market data to quickly generate actionable insights for agile, strategic business decisions. ‘AI is rapidly reshaping all industries, but none more than financial institutions,’ said Rob Hegarty, General Manager of Financial Markets and Fintech at DataRobot. ‘So much of the world’s market data is in kdb+ due to its powerful time series capabilities and performance. The integration with DataRobot—the world leaders in enterprise AI at scale—will accelerate ROI and separate financial markets institutions’ performance from their peers’.”
The release continues, “In the AI-driven era, organizations everywhere need to develop strategies for implementing powerful machine learning models in order to stay competitive. This pressure is particularly acute in the securities industry, where financial institutions are constantly seeking ways to use cutting-edge technologies to accelerate research, find competitive advantage in trading, improve alpha generation, and manage risk. However, there are several hurdles when it comes to successfully deploying AI solutions. For instance: The sheer volume of financial market data, which makes searching for signals and developing predictive models extremely challenging and time-consuming; A reliance on manual, inefficient processes to build models for highly volatile and time-critical applications; and Fragmented, distributed data sources across tools, teams, and platforms, making it difficult to source and combine data for use in the development and deployment of AI.”
Read more at Business Wire.
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