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Demo: Generative AI for modern data management

Science & Technology

Introduction

Thanks to exciting announcements by Cohesity, Microsoft, and the future of AI for the enterprise, a unique platform is being unveiled that combines data management, data security, and Data Insights powered by generative AI. This article dives into how Cohesity's architecture is designed to revolutionize technology and provide cutting-edge advancements for end-users.

Greg, the host, and Mohit from Cohesity discuss how the platform is architected to do more than just store data but to enhance it with capabilities like generative AI. Cohesity's distributed file system, metadata management, and indexing provide the foundation for bringing AI directly to the platform, making data AI-ready for users.

Keywords

Cohesity, generative AI, data management, AI-ready data, distributed file system, metadata management, indexing, modern data solutions, AI architecture.

FAQ

  1. What makes Cohesity's platform unique for data management and AI integration?
  • Cohesity's architecture is designed to go beyond traditional data storage, enabling the integration of generative AI and providing AI-ready data for users.
  1. How do traditional data solutions differ from Cohesity's approach to modern data management?
  • Traditional solutions often lack the architecture to run computation close to the data, making it challenging to embrace AI capabilities. Cohesity's platform is designed to bring AI capabilities closer to where the data resides.
  1. How does Cohesity leverage AI for data insights?
  • Cohesity's platform uses generative AI to sift through large amounts of data, providing insights for tasks like compliance monitoring and governance. Users can interact with AI models to gain specific insights from their backups.
  1. What are the benefits of using Cohesity's AI-powered Data Insights platform?
  • By harnessing the power of AI, users can extract valuable insights from their data without the need for complex query languages or advanced reporting features. This humanizes the interaction between users and machine learning models.