Your Guide to the AWS Machine Learning Summit

We’re about a week away from the AWS Machine Learning Summit and if you haven’t registered yet, you better get on it! On June 2, 2021 (Americas) and June 3, 2021 (Asia-Pacific, Japan, Europe, Middle East, and Africa), don’t miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit. This Summit, which is open to all, brings together industry luminaries, AWS customers, and leading ML experts to share the latest in ML. You’ll learn about science breakthroughs in ML, how ML is impacting business, best practices in building ML, and how to get started now without prior ML expertise. This post is your guide to navigating the Summit.

The day kicks off with a keynote from ML leaders from across AWS, Amazon, and the industry, including Swami Sivasubramanian, VP of AI and Machine Learning, AWS; Bratin Saha, VP of Machine Learning, AWS; and Yoelle Maarek, VP of Research, Alexa Shopping, who will share a keynote on how we’re applying customer-obsessed science to advance ML. You’ll also hear from Ashok Srivastava, Senior Vice President and Chief Data Officer at Intuit, about how the company is scaling its ML and AI to create new customers experiences.

Next, tune in for an exclusive fireside chat with Andrew Ng, founder and CEO of Landing AI and founder of deeplearning.ai, and Swami Sivasubramanian about the future of ML, the skills that are fundamental for the next generation of ML practitioners, and how we can bridge the gap from proof of concept to production in ML.

From there, pick the track that best matches your interests or mix and match throughout the day. We’ll also have expert Q&A available from 11:00am–3:00pm local time.

The science of machine learning

If you’re an advanced practitioner or just really interested in the science of ML, this track provides a technical deep dive into the groundbreaking work that ML scientists within AWS, Amazon, and beyond are doing to advance the science of ML in areas including computer vision, natural language processing, bias, and more.

Speakers include two Amazon Scholars, Michael Kearns and Kathleen McKeown. Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair. He is co-author of the book “The Ethical Algorithm: The Science of Socially Aware Algorithm Design,” and joined Amazon as a scholar June 2020. McKeown is the Henry and Gertrude Rothschild professor of computer science at Columbia University, and the founding director of the school’s Data Science Institute. She joined Amazon as a scholar in 2019.

You’ll also get an inside look at trends in deep learning and natural language in a powerhouse fireside chat with Amazon distinguished scientists Alex Smola and Bernhard Schölkopf, and Alexa AI Senior Principal Scientist Dilek Hakkani-Tur.

The impact of machine learning

If you’re a technical business leader, you won’t want to miss this track where you’ll learn from AWS customers that are leading the way in ML adoption. Customers including 3M, AstraZeneca, Vanguard, Carbon Lighthouse, ADP, and Bundesliga will share how they’re applying ML to create efficiencies, deliver new revenue streams, and launch entirely new products and business models. You’ll get best practices for scaling ML in an organization and showing impact.

How machine learning is done

If you’re a data scientist or ML developer, join this track for practical deep dives into tools that can speed up the entire ML lifecycle, from building to training to deploying ML models. Sessions include how to choose the right algorithms, more accurate and speedy data prep, model explainability, and more.

Machine learning: No expertise required

If you’re a developer who wants to apply ML and AI to a use case but you don’t have the expertise, this track is for you. Learn how to use AWS AI services and other tools to get started with your ML project right away, for use cases including contact center intelligence, personalization, intelligent document processing, business metrics analysis, computer vision, and more. You’ll also learn from customers like Fidelity on how they’re applying ML to business problems like DevOps.

For more details, visit the website and we’ll see you there!

About the Author

Laura Jones is a product marketing lead for AWS AI/ML where she focuses on sharing the stories of AWS customers and educating organizations on the impact of machine learning. As a Florida native living and surviving in rainy Seattle, she enjoys coffee, attempting to ski and enjoying the great outdoors.