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.
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.
What you'll learn
- Explore how you can deploy complex distributed data analytics and machine learning applications at scale on containers with enterprise-grade security and performance in production
Anant Chintamaneni is Vice President and General Manager of BlueData Products at HPE (via BlueData acquisition), where he’s responsible for product and go-to-market (GTM) strategy to help enterprises on their digital transformation journey with next generation hybrid cloud software. Anant has more than 19 years of experience in enterprise software, business intelligence and big data and analytics infrastructure. Previously, Anant led product management teams at Pivotal, Dell EMC, and NICE.
Matt Maccaux is the Global Field Chief Technology Officer at BlueData, where he focuses on helping enterprise organizations define and implement their enterprise-wide initiatives for AI, big data, and analytics and works closely with executives at enterprise customers to develop their road map and strategies for data-driven digital transformation using AI, ML, and advanced analytics. He helps these customers to accelerate their time to market with AI, ML, and analytics, with an enterprise-wide platform to provide those capabilities as a service to their data science teams. Previously, he worked worked with leading enterprises across many industries for the past twenty years in a variety of roles at some of the biggest technology companies in the world.
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