Prerequisite: None
McKinsey predicts ten to fifteen trillion dollars of value will be unlocked by AI and ML over the next seven years as these technologies are embedded in virtually every business process and applied to databases throughout the enterprise.
Although many companies are investing in these technologies, relatively little attention has been given to the data platform needed to enable them at scale. Data scientists and machine learning specialists typically focus on the tools and algorithms that they want to use, giving little or no thought to the data platform.
However, the data platform and the strategies used to create the analytics data repository (data lake, data lakehouse, or data warehouse) are—or should be—the foundation of the AI/ML program. With the right approach to the platform and the database, most advanced analytics can be performed at scale inside the database. Further, a feature store shared by multiple machine learning models can be maintained inside the database, and similarity search can be supported inside the database with a vector store or vector index.
Most likely, it is only via efficient, scalable in-database AI/ML that most business strategies can be affordable at all. In addition, the in-database approach confers advantages in security, consistency, and cycle time. This talk will be about the strategies for the data platform as an enabler of AI/ML and the benefits that can be realized with a strategic approach.