Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
Business professionals need straightforward ways to first discover and then
Today, “doing more with less” is a key principle driving business strategy across many resource-intensive industries. Organisations are looking to get more out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify
Seizing the AI opportunity to tap new sources of energy inspired one ExxonMobil leader to take a collaborative approach to its big data problem. Now she’s been recognized by IBM as a top woman AI leader.
AutoAI, a powerful automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award, chosen by a panel of 13 independent judges yesterday for the AIconics AI Summit in San Francisco.
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
In part one of the Capitalogix data science story, I focused on their strategic need for a data platform that supports speed, data variety and custom-built algorithms to find advantages for their business. A key success driver: they worked to make life better for the people on the front lines of
With the automated AI and ML advancements, you may find yourself wondering--what are the overall impacts to business? How will all of this technological progress impact the ways we run our business and perform our jobs?
This week's guest is Jorge Castanon, a senior data scientist for Watson Studio at IBM. Host Al Martin and Jorge discuss some typical data problems currently plaguing the industry -- and how Watson Studio makes dealing with those problems that much easier. Get ready for an in-depth, technical
Together, IBM and Cloudera offer a modern data platform with the governance and security to drive the future of AI and ML. Our solutions are optimized for the cloud, but we give our customers options to put their data where it works best for them.
Back when I was in school, one of the most difficult classes for my business degree was quantitative analysis. It wasn’t just hard, it was laborious to translate and solve business conditions and problems into algebraic equations by hand. In the beginning, it was merely optimizing output based on a