Machine learning for business: how to make it a success
Ralf-Dieter Wagner, Lead Go-to-Market AI ML at AWS, reveals what the most common difficulties are when implementing machine learning. And how to successfully overcome them.
Machine learning (ML) is on everyone's lips and more and more companies are using this technology – be it to optimise the customer experience or to predict demand. However, that is not as easy as it sounds: "We see customers struggling with the application of machine learning," says Ralf-Dieter Wagner, Lead Go-to-Market AI ML at AWS. Especially issues such as scaling, data security and AI explainability are giving companies headaches.
According to Wagner, all these challenges can be overcome with the right approach: This includes thinking of ML as more than just a technology, knowing where the use of ML makes sense in the first place and putting together a team that combines all the relevant know-how for an ML implementation.
But what is ML if not just a technology? How does one find out for what purpose ML is best used? And who belongs in a functional ML team? Wagner answers these questions in his "Sparx" talk.
RALF-DIETER WAGNER
Ralf-Dieter Wagner (*1967) is based in Munich, Germany and is responsible for the go-to-market for the AWS AI /ML service portfolio, focusing on the DACH region. In his role, Wagner enables clients across industry verticals to leverage data and AI/ML for better business outcomes and consults them on how to get started quickly and scale. Prior to joining AWS, he served as General Manager EMEA for the US-based AI start-up r4, and prior to that he spent more than 20 years at Accenture, as a partner.
