As a Global AI & Machine Learning Evangelist, Julien focuses on helping developers and enterprises bring their ideas to life. He frequently speaks at conferences, and blogs on the AWS Blogs and on Medium. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in top-tier web startups where he led large Software and Ops teams in charge of thousands of servers worldwide. In the process, he fought his way through a wide range of technical, business and procurement issues, which helped him gain a deep understanding of physical infrastructure, its limitations and how cloud computing can help.
Over the years, cloud computing has proven to be the most agile, scalable and cost-effective option to build and run IT applications. Of course, these benefits can also apply to Machine Learning (ML). In this hands-on session, we'll start with a quick introduction to the different layers of the AWS ML stack. Then, we'll dive into the different solutions that you can use to build, train and deploy your ML applications, using either Amazon EC2 instances, container services, Amazon SageMaker, and high level services like Amazon Forecast and Amazon Personalize. Services will be demoed using the AWS console and AWS SDKs. Along the way, we'll also compare them from a skill, technical, operational and cost perspective. In order to fully enjoy this session, you should have a basic understanding of core AWS concepts (EC2, S3, IAM, etc.). We'll use simple ML examples, so no deep ML knowledge is required.
Scheduled on Saturday from 10:00 to 12:00 in Stream 3