Category: Amazon Machine Learning

As organizations deploy generative AI technologies, they face challenges including lack of expertise, fragmented governance, unclear accountability, and immature tooling—issues that can be addressed through an integrated framework of governance mechanisms, repeatable processes, and embedded safeguards.

Machine Learning (ML) is such a powerful new set of technologies, opening up so many business possibilities, that it’s sometimes difficult to imagine how best to put it to use. The possibilities are overwhelming, and there is little history to build on. In this post Baran Karlidag ties ML to the specific challenges that financial […]

Like other industries, insurance must respond to rising customer expectations for digital interactions. In this post, Tony Jacob, Amazon Web Services’ Worldwide Insurance Business Development Manager, explains how insurers can deepen their relationships with customers by engaging them digitally. Interestingly, he finds that the challenges have less to do with the digital interactions themselves, and […]

“He uses statistics as a drunken man uses lamp posts—for support rather than for illumination.” —Andrew Lang When it comes to the data, it is culture first and capability second. In my previous post, I talked about how executives can create a data-driven culture in their enterprises. In this post, I am going to dive […]

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” ― Jim Barksdale Conversations about data-driven enterprises often focus on tools, big data, and the technological advancements that have made storing, processing, and analyzing data faster and cheaper. While these are all important, creating a data-driven culture […]