DataStax Launches Luna ML Service to Build AI Capabilities

• Blockchain Council
• May 10, 2023

DataStax, a leading provider of database management solutions, has launched a new service called Luna ML, aimed at simplifying real-time AI deployment. The service will allow organizations to build artificial intelligence (AI) capabilities quickly and easily without the need for complex data preparation or engineering.

Luna ML leverages the power of Apache Cassandra, a distributed NoSQL database, to provide a scalable, high-performance platform for machine learning (ML) workloads. With Luna ML, data scientists, and engineers can build and deploy machine learning models directly within their existing database infrastructure without having to move data to a separate analytics platform.

The launch of Luna ML marks a significant step forward for organizations looking to leverage the power of AI. According to a recent report by Gartner, “By 2025, 75% of all enterprise data will be created and processed outside the traditional, centralized data center or cloud.” With Luna ML, organizations can take advantage of this trend and build AI capabilities directly into their data infrastructure, enabling them to make real-time, data-driven decisions at scale.

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One of the key features of Luna ML is its ability to simplify the process of feature engineering. Feature engineering is the process of selecting, extracting, and transforming relevant data from raw data sets, which is a critical step in building accurate and effective machine learning models. Luna ML automates much of this process, allowing data scientists and engineers to focus on building models rather than preparing data.

“Luna ML is a game-changer for organizations looking to build AI capabilities quickly and easily,” said Billy Bosworth, CEO of DataStax. “By simplifying the process of real-time AI deployment, we’re empowering organizations to make smarter, data-driven decisions at scale.”

In addition to its feature engineering capabilities, Luna ML also includes a range of other features designed to simplify machine learning workflows, including pre-built ML models, automated model selection, and real-time model monitoring.

DataStax’s decision to build Luna ML on top of Apache Cassandra reflects the growing trend of using distributed databases as a foundation for AI and machine learning workloads. With Luna ML, organizations can take advantage of the scalability and performance of Cassandra while building powerful machine-learning models.

Overall, Luna ML represents a significant step forward for the AI industry, providing a powerful new tool for organizations looking to build AI capabilities quickly and easily. With its focus on simplifying real-time AI deployment and feature engineering, Luna ML is poised to become a game-changer in the world of AI and machine learning.

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