Many organizations are increasing their data analytics investments to target building intelligent applications. Whether these efforts fully meet an agreed definition of artificial intelligence (AI) is less important than the realization that these investments pay measurable returns. Despite increasing complexity, organizations are cutting costs and improving outcomes by building software that is powered by machine learning models to reduce errors and produce more consistent results. The recent explosion of data analytics work has produced numerous interesting uses cases in every sector of our society and economy. Financial institutions have software to detect possible credit card fraud and identify transactions that might be associated with money laundering. Hospitals provide doctors and radiologists with “smart” software that can independently evaluate medical images.
Almost every organization that has an AI success story admits that it was not easy. Assembling the right mix of people, data sources, computing resources, and team structure to develop intelligent software applications with good ROI is a new challenge. We believe that the abundance of recent AI success stories is encouraging more organizations to invest in the development of intelligent applications, often in areas that involve significant experimentation and risk.
Companies like Google, Facebook, Uber, and LinkedIn created their business value by focusing on the use of data analytics in all their applications, choosing to build all their own infrastructure and development, deployment, and life-cycle tooling. Most of these companies have contributed some of their internal development efforts to the open-source community. While that is an obvious choice, there is no comprehensive open-source “AI platform” for developing the data analytics models that are needed to create intelligent applications. The commercial software development community fills the gap for organizations that do not have the extra time and resources to build an AI platform.
As your teams develop and deliver models, they need flexible infrastructure, immediate access to the latest innovative tools, and methods to collaborate. Domino provides a single enterprise platform with purpose-built functionality for each step of the life cycle. Your sales organization has a CRM and your recruiting organization has an Applicant Tracking System—Domino Data Science Platform is the analogous "system of record" for data science work. Dell EMC provides validated building blocks that are tuned and optimized to provide the best performance.