Sri-Jagadish(Jag) Baddukonda’s Post

This white paper, "Systems of Automation", published by DataStax in 2024, discusses the transformative potential of AI agents in enterprise architecture. The white paper underscores that Systems of Automation are the future of enterprise architecture. By integrating AI agents, enterprises can transcend traditional automation limits, achieving greater adaptability, efficiency, and scalability. Organizations that embrace these agentic workflows will thrive in an increasingly complex and fast-paced digital environment. DataStax powers generative AI applications with real-time, scalable data solutions. It provides tools like Astra DB vector database and integrates seamlessly with enterprise systems to enable smart, production-ready AI applications. #datastax

Outdated data architectures are hindering advancement in the #AI age. Learn how a distributed data architecture can break down barriers, minimize latency, and accelerate #digitaltransformation. Explore essential strategies and practical applications in our newest blog: https://okt.to/YP4hq2 #TheDataMeetingPlace #WhereTomorrowComesTogether #PlatformDIGITAL

GenAI is business-ready, but is your business GenAI-ready? AI’s data demands are enormous, often requiring new software and hardware to succeed. Businesses should prepare by ensuring their data architecture is seamlessly integrated and adaptable. Next, they need to evolve their data storage setup, which may involve connecting silos and harmonising data lake and data warehouse architecture. The four key trends for data management in 2024 are adaptable data architecture, strategic data storage choices, innovative data engineering for integration, and comprehensive deployment practices. A good read! #AI #dataengineering #digitaltransformation

Legacy data architectures are limiting progress in the #AI field. Discover how adopting a distributed data architecture can eliminate silos, reduce latency, and speed up #digitaltransformation. Dive into key strategies and real-world applications in our latest blog: https://okt.to/KYkFB6 #TheDataMeetingPlace #WhereTomorrowComesTogether #PlatformDIGITAL

Outdated data infrastructures are impeding progress in the #AI age. Discover how implementing a distributed data architecture can eliminate silos, reduce latency, and drive #digitaltransformation. Visit our latest blog for key strategies and practical examples: https://okt.to/usVDvX #TheDataMeetingPlace #WhereTomorrowComesTogether #PlatformDIGITAL

Two out of two for SAS Viya from IDC in their #MLOps evaluation. Not only has the Futurum Group recently analyzed and reviewed the performance results of SAS Viya and concluded “The Performance of SAS Viya was impressive, they didn’t just outperform competing AI/ML libraries, they crushed the competition.” http://2.sas.com/6045ssDjB Now SAS is also recognized by the IDC MarketScape for #MLOps! 💡 The report states: "SAS Viya's strength in performance and scalability is rooted in its in-memory architecture, parallel processing capabilities, efficient access to data sources, and breadth of deployment options including real time. In addition, Viya's cloud-native design allows it to scale horizontally and vertically, enabling the software to handle large data sets and complex analytics tasks efficiently." #AnalystReport http://2.sas.com/6046ssDj8

Your AI strategy is only as good as the data that powers it. For large enterprise, that data is *everywhere* and open architectures are the best way to access it. Read more from Ethel Anderson and I here: Starburst

It's not rocket science. It's #datascience. #Mastercard leveraged #RedHat #OpenShift to build a robust, scalable, and flexible architecture that enables rapid experimentation and deployment of #artificalintelligence (#AI) / #machinelearning (#ML) workloads. With its new platform that uses #containerization, #cloudnative tools, and automated deployment pipelines to deliver faster model training times, scalability of resources, and enhanced support for complex #machinelearning (#ML) and #deeplearning (#DL) projects, the company transformed its Data Science Workbench (#DSWB). See for yourself how the global payment technology company improved operational efficiency, reduced time-to-market for new models, and increased innovation in AI and ML: https://red.ht/4gjYpQo. #OpenShiftCommons #KubeConNA #CloudNativeCon #Commons #KubeConNA #Kubernetes

Advance your AI initiatives and maximize performance for model training with a unified data storage architecture. Read how AI company Pong Yuen accelerates innovation with help from the NetApp data storage platform.

NAND Research's Steve McDowell working with WEKA has put together a killer 'buyer's guide' of sorts for enterprises looking to deploy #AI solutions on top of legacy storage architectures. It has loads of useful info on the demands placed on those kinds of data infrastructures and how storage architectures have to evolve. Take a look: