How do baseball scouts use machine learning and AI to predict player performance? Ari Kaplan, Principal at Aginity, and David Kearns, Offering Manager, IBM Analytics Ecosystem, join us to talk about the recent merge of H20.ai and IBM. They also discuss how baseball decisions are made using
There’s a lot to love about open-source technology. Based on the idea that a community of people can iterate on and improve something better than a single person, team, or even company, open-source promises continuous innovation and community support.
On this week's episode, John J Thomas, Distinguished Engineer and Director for IBM Analytics, and Steve Moore, Senior Content Designer and Story Strategist, join us to talk about data science and how your business can best weave the skills of a data scientist into their decisions. Learn how to
Purchasing options outside of the office are diverse and varied depending on what people want to buy, where and when they buy it, and what they need it for. While shoppers might have personal preferences, they don't limit ourselves to one retailer for all purchasing decisions. So why do that in a
By 2025, there will be 180 trillion gigabytes of data in the world, compared to only 10 trillion gigabytes in 2015. Of this, 90 percent will be unstructured, which is why many organizations are adopting open source data lake technologies such as Apache Hadoop to handle this expanding volume and
Join us 27 February at 1 PM ET for "Machine Learning Everywhere: Build Your Ladder to AI." Visit the event landing page to learn more about the event and register for a calendar reminder: ibm.com/mleverywhere
Readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read blog posts about incorporating machine learning, choosing the best possible data model, determining how to make the most of data science skills, working with open source frameworks and more.
The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge. Learn how to deliver real-time feeds of transactional data from mainframes and distributed environments directly into Hadoop clusters and make constantly changing data more available.
Managing enterprise information has always been a good idea, however with the potential for looming penalties from the General Data Protection Regulation (GDPR) non-compliance, companies are waking up and some organizations are even seeing GDPR as an opportunity to establish strengthened
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
What is driving change in the world of data? In his keynote from the Big Data Summit KC 2017, our Making Data Simple podcast host and IBM Analytics VP Al Martin addresses disruption, the data maturity model and the five areas business must get right to succeed in the era of cognitive computing.
How did companies like Facebook and Airbnb get so big so fast? What can we learn from them? Why is data so important for growth? Nancy Hensley, Director of Strategy & Growth for IBM Hybrid Cloud, has the answers in this episode of Making Data Simple.
Although there are many new and emerging classes of data integration, quality and governance software tools available in the market, many large organizations are coming to the conclusion that they're best served by a single unified enterprise data integration, quality and governance platform that
In this episode of Making Data Simple we hear insights from IBM Machine Learning Hub data scientists Jorge A. Castañón and Óscar Lara-Yejas as they discuss what machine learning is and is not. They also answer the most controversial question today: Will machines take our jobs? Come find out!