Look for Adaptive ML to find increased adoption across a spectrum of use cases defined by how rapidly changing their contextual data, conditions, and actions are. For example, combining cyber risk and remote site risk assessments in an adaptive ML model is a use case that utility companies are using in production today. Adaptive ML’s greatest gains could come from manufacturing, where combining telemetry data from visual IoT sensors with adaptive ML-based applications can identify defective products immediately and pull them from the production line. Saving customers the hassle of returning defective products can increase customer loyalty while reducing costs. Given the chronic labor shortage manufacturers face, combining Adaptive ML techniques with robotics can help manufacturers still meet customers’ needs for products consistently. Adaptive ML is also the basis of autonomous self-driving vehicle systems and collaborative, smart robots that quickly learn how to complete simple tasks together through iteration. DSML platform vendors known for their expertise include Cogitai, Google, Guavus, IBM, Microsoft, SAS, Tazi, and others.
Read full original article published on VentureBeat https://venturebeat.com/2022/01/10/how-to-build-a-data-science-and-machine-learning-roadmap-in-2022/