Put Deep Learning to work: Accelerate Deep Learning through Amazon SageMaker and ML Services
Deploying deep learning (DL) projects are becoming increasingly more pervasive at enterprises and startups alike. At Amazon, Machine Learning University (MLU)-trained engineers are taking DL to every aspect of Amazon’s businesses, beyond just Amazon Go, Alexa, and Robotics. In this workshop, Wenming Ye (AWS), Rachel Hu (AWS), and Miro Enev (Nvidia) offer a practical next step in DL learning with instructions, and hands-on labs using the latest Nvidia GPUs and AWS Inferentia. You will explore the current trends powering AI/DL adoption, powerful new GPU/AWS Inferentia accelerator instances, distributed training and inference optimization in neural networks.
Wenming Ye is an AI and ML specialist architect at Amazon Web Services, helping researchers and enterprise customers use cloud-based machine learning services to rapidly scale their innovations. Previously, Wenming had a diverse R&D experience at Microsoft Research, SQL engineering team, and successful startups.
Rachel Hu is an applied scientist on the AWS AI working on deep learning. She received her master’s degree of statistics from University of California, Berkeley. She is an instructor at Amazon Machine Learning University and frequently presents at external events such as AWS Re:invent, Nvidia GTC, etc. She enjoys empowering everyone who is curious about start-of-the-art deep learning algorithms with easy to understand instructions and innovative new teaching tools. Before joining Amazon, Rachel also worked on natural language processing projects to promote user engagements in multiple industries.
Miro Enev is a principal solutions architect at NVIDIA, specializing in advancing data science and machine intelligence. He is part of a team focused on industrial IoT solutions, and is dedicated to supporting AWS applying the latest AI research to the challenges of modern business. Miro studied Cognitive Science and Computer Science as an undergraduate at UC Berkeley. He also holds a Ph.D. from the University of Washington’s Computer Science and Engineering Department where his thesis was on Machine Learning Applications for Information Privacy in Emerging Sensor Contexts.