The goal of efforts like GPU-powered machines on-premises and in the cloud and custom-built machines for training deep neural networks and other AI-powered analytics is to empower AI experts to accelerate the delivery of new AI experiences across agents, apps, and services. In recent years, the cloud has matured to enable Fortune 500 companies to effectively run their businesses at an unprecedented scale and elasticity, giving them the ability to spin up new resources whenever there is demand and spin down resources for cost savings. In recent years, the rise of container services has also dramatically changed the way we think about the lifecycle of applications.
Taking its benefits into account (e.g., the ability to empower AI practitioners with rapid experimentation environment and accelerate the delivery of new AI experiences), the cloud offers immense potential as an AI supercomputer that can do deep learning and many AI-related analytics task faster, better, and simpler. Joy Qiao and Wee Hyong Tok explore the trends that motivated businesses to use the cloud to do AI and the exciting opportunities that lie ahead. They also share lessons learned and put these lessons into concrete framework for understanding the technologies and services that can enable you to build, use, and do AI using the cloud. Joy and Wee Hyong then demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications.
Join in to learn how to use the cloud to rapidly create an environment for doing AI and how to scale the environment as your workload and the data that you are processing grows. Along the way, Joy and Wee Hyong also explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow.
Wee Hyong Tok is a principal data science manager for the cloud AI team at Microsoft, where he works with teams to cocreate new value and turn each of the challenges facing organizations into compelling data stories that can be concretely realized using proven enterprise architecture. Wee Hyong has worn many hats in his career, including developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique super powers to nurture and grow high-performing innovation teams that enable organizations to embark on their data-driven digital transformations using artificial intelligence. He has a passion for leading artificial intelligence-driven innovations and working with teams to envision how these innovations can create new competitive advantage and value for their business and strongly believes in story-driven innovation. He coauthored one of the first books on Azure Machine Learning, Predictive Analytics Using Azure Machine Learning, and authored another demonstrating how database professionals can do AI with databases, Doing Data Science with SQL Server.
Joy Qiao is a Senior Solution Architect in the AI & Research Group at Microsoft, where she is responsible for driving end-to-end AI and Machine Learning solutions on Azure among the partner eco-system. Joy has over 15 years of IT industry experience including 11 years at Microsoft working as technical lead/architect roles in various Microsoft Azure teams, as well as senior consultant/architect in the Microsoft services team. Joy has mainly been focusing on Microsoft Azure, Big Data and Machine Learning technologies, leading and delivering various Machine Learning, Big Data and Cloud-based solutions for both internal and external MS enterprise customers and partners.
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