There is a growing need for versatile, hybrid architectures that can combine the best of both data warehousing and big data analytics. The cloud is the perfect solution, because it makes it easier to build a robust data warehouse as a central “hub”, and then add other environments that can be
The reality is that AI is still heavily-reliant upon smart, willing and trained humans in order for AI to behave in a manner that we would expect. Humans are needed to scope the problems, identify relevant examples and verify the results. Without humans as a guide, current AI is no more capable
Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example
January marked the release of the long awaited Hidden Figures movie featuring an all-star cast and highlighting the contributions of both women and IBM's technology to history. Hidden Figures tells the true story of three African-American female mathematicians, Katherine Johnson, Mary Jackson, and
Jeff Josten is IBM Distinguished Engineer for DB2 for z/OS Development, IBM Analytics, Platform Development. In this podcast, he discusses how the value of machine learning in enterprise applications of hybrid transaction/analytics processing. He will be speaking on this topic on February 15, 2017
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
J White Bear is a data scientist and software engineer at IBM. In this podcast, White Bear discusses simultaneous localization and mapping, an ongoing research area in robotics for autonomous vehicles and well-recognized as a nontrivial problem space in both industry and research.
IBM’s community of big data developers continues to grow. As our Big Data Developer meetup program moves into its fifth year, this worldwide community of customers, partners and IBM developers is on the verge of enlisting its 100,000th member—when we published this blog, we counted 99,100.
Seth Dobrin is vice president and CDO, IBM Analytics, platform development, at IBM. In this podcast, Dobrin shares experiences using Apache Spark for data science transformation and some thoughts on a larger vision for data science transformation at scale.
Steven Astorino is Vice President, Development, IBM Private Cloud Analytics Platform. In this podcast, he discusses how machine learning is driving the evolution of data science in strategic business initiatives.