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
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
It’s been one year since we launched IBM Cloud Pak for Data (previously IBM Cloud Private for Data), IBM's data and AI platform for today's modern enterprise. Since then, this platform has been embraced by hundreds of customers, and Forrester ranked it No. 1 in their “Enterprise Insights Platform”
IBM Cloud Pak for Data System is an integrated end-to-end platform that is cloud native by design, architected as microservices and containerized workloads. It offers instant pre-assembled provisioning and has capabilities to collect, organize and analyze data. It takes the IBM Cloud Pak for Data
Companies are entering “chapter two” of their digital transformation. The next chapter is all about moving from experimentation to true transformation. It’s about gaining speed and scale. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI as core
Learn more about Brittany Bogle in our new series profiling the technical experts helping clients reach their AI and machine learning goals. Her path to data science elite status is what makes her a valuable and unique practitioner for IBM clients.
This week on Making Data Simple, Dinesh Nirmal, vice president, IBM Data and AI Development, joins "Making Data Simple" to discuss current industry trends. Host Al Martin poses questions that are both technical and leadership-oriented. They discuss new, emerging technologies and swap their own
Predictive modeling and analytics have long been the domain of the data scientist and only the data scientist. But with modern tools, data science is becoming a team sport—business analysts and subject matter experts can join the analysis. While the players may have different skill sets and