A modern data platform must evolve with the goal of creating a single version of the truth for all data management activities, including AI-infused data operations and AI model management. A single, unified view of the data estate and all data management activities delivers centralized controls and monitoring while improving reliability and resiliency. Key to the success of large-scale AI programs are governance and trust policies and practices that ensure AI models are not opaque, but explainable, and are continuously monitored for performance as well as for model decay and drift.
“You need to follow best practices around machine learning lifecycle engineering to continuously deliver value to the end consumers of insights,” Kamat explains. “It’s not a one-time thing for data to provide value at scale or for models to be employed for industrial consumption.”
1. Conduct a rigorous and in-depth evaluation of your current landscape
2. Develop a clear assessment of the business drivers for change and appetite for investment
3. Create realistic blueprints for transforming legacy platforms and mapping on-premises-to-cloud and edge-to-cloud integrations
4. Deploy a composable data fabric comprising a set of architecture blueprints and best practices
5. Utilize a scalable AI services framework