How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE)
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.
Thomas Phelan is cofounder and chief architect of BlueData. Previously, a member of the original team at Silicon Graphics that designed and implemented XFS, the first commercially availably 64-bit file system; and an early employee at VMware, a senior staff engineer and a key member of the ESX storage architecture team where he designed and developed the ESX storage I/O load-balancing subsystem and modular pluggable storage architecture as well as led teams working on many key storage initiatives such as the cloud storage gateway and vFlash.
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