Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.
S4 Agtech, which offers risk management solutions for agriculture companies, migrated to Google Cloud to save money and time, as well as, scale databases and machine learning models faster. The company uses BigQuery as its data warehouse, and has dramatically reduced storage and processing costs by 80%, while providing customers their analytics results 25% faster. S4 has also gained more time for innovation in helping its customers de-risk crop production, including updating and improving algorithms.