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
A new Harvard Business Review Analytic Services and IBM study shows 72% of line of business leaders believe they are susceptible to disruption within three years but only 14% are prepared to respond. This presentation provides a lens into the thinking of 600 business executives and examines the
Dinesh Nirmal is Vice President, IBM Analytics Platform Development. In this podcast, he discusses the role that machine learning plays in enterprise cognitive analytics initiatives. He will be speaking on this topic on February 15, 2017 at the IBM Machine Learning Launch Event.
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
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.
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.
In this podcast, Rob Thomas, general manager, IBM Analytics, discusses how investments in machine learning within private cloud deployments can contribute to customer business success. Thomas will speak on this topic, 15 February 2017, at the IBM Machine Learning Launch Event.
In this white paper, discover how programmers and data scientists can use SparkR to transform R into a tool for big data analytics, taking advantage of parallel processing and near-linear scaling to tackle much larger challenges than would normally be possible with other methods.
Holden Karau is a software engineer at IBM, an active open source contributor and coauthor of Learning Spark (O'Reilly Media, February 2015) and the soon to be released High Performance Spark (O'Reilly Media, March 2017). In this podcast, Karau examines how to effectively search logs from Apache
Nick Pentreath is a principal engineer at IBM, a member of the Apache Spark project management committee (PMC) and author of Machine Learning with Spark (Packt Publishing, December 2014). In this podcast, Pentreath covers the basics of feature hashing and how to use it for all feature types in
Today’s businesses need a culture of collaboration that empowers knowledge workers to glean cognitive insights from data that help transform and modernize operations. See how cloud-based platforms and solutions enable data scientists and other experts to exploit artificial intelligence, machine