How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE (BlueData))

Anant Chintamaneni and Matt Maccaux explore 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.

Anant Chintamaneni and Matt Maccaux explore 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. 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 is 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. This session will focus on how to make it work.