During the IBM flagship Think conference in San Francisco today, businesses looking to accelerate their transformation with the IBM AI Watson were treated to news that they’ll be able to build, deploy and run AI models and applications across any cloud, giving them the freedom to apply Watson
Watch to learn how to drive faster insights as machine learning accelerates data classification and quality. Featuring Madhu Kochar, VP Analytics Development, IBM and Anantha Narasimhan, Program Director, IBM.
Are you ready for machine learning? 2018 is shaping up to be the year machine learning gains widespread implementation as enterprises prepare for the future of artificial intelligence. Learn how to accelerate your journey with a fast, scalable approach to machine learning that will give your
Smart companies are finding new ways to squeeze more value out of their massive data storehouses. They’re unlocking insights from their data that build new business models, improve customer experiences and outpace competitors. So where do these business-changing insights come from?
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their
Connect with the people pioneering where data goes next in person or join the live stream June 22, 2017. Learn how to leverage all the elements of data and analytics and secure your competitive advantage with machine learning. Register today.
IBM Analytics VP of Marketing Jeff Spicer sits down with Data Scientist and evangelist Dez Blanchfield to recap IBM InterConnect 2017 and give his insights into a few of the announcements from this year's event.
Data science is a team sport that involves specialists with complementary skills and aptitudes. Successful data science initiatives leverage high-performance team collaboration. Like the fictional sleuth and his partner, IBM’s customers in the data science community must have the right mix of
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source