How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE)
Join Thomas Phelan to learn whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.
| Talk Title | How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE) |
| Speakers | Thomas Phelan (HPE BlueData) |
| Conference | O’Reilly Artificial Intelligence Conference |
| Conf Tag | Put AI to Work |
| Location | London, United Kingdom |
| Date | October 15-17, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
AI, ML, and data analytics are transforming every industry. When Gartner recently asked CIOs which technologies are game-changers for their organization, AI and ML are at the very top of the list; data analytics is number two. At the same time, containerization is taking the enterprise by storm-driven by the benefits of greater agility, efficiency, and portability across any infrastructure. By 2022, more than 75% of global organizations will be running containerized applications in production, up from less than 30% today. As a result, many companies are now exploring whether it is possible to deploy complex distributed data analytics and ML applications (like Cloudera, Spark, Kafka, H2O, and TensorFlow) at scale on containers—with enterprise-grade security and performance in production. Thomas Phelan explains how to make it work. This session is sponsored by HPE.
