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On demand: The 2021 AWS Machine Learning Summit

Didn't get the opportunity to attend the summit earlier this month? Now available on demand: Presentations on the science of machine learning by leading scholars, a fireside chat with Andrew Ng, and more career-growth content.

By Staff writer
June 28, 2021
1 min read
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Amazon Web Services (AWS) held a free virtual Machine Learning Summit on June 2, with the goal of bringing together customers, developers, and the science community to learn about advances in the practice of machine learning (ML).

Opening keynote

The event began with a keynote from from Swami Sivasubramanian, AWS vice president of machine learning; Bratin Saha, vice president of AWS Machine Learning Services; and Yoelle Maarek, vice president of research science, Alexa Shopping.

AWS ML Summit 2021 | Opening Keynote

Fireside chat

Next up was a fireside chat. Andrew Ng, founder and CEO of Landing AI, and Sivasubramanian discussed the future of ML, the skills that are fundamental for the next generation of ML practitioners, and how to bridge the proof-of-concept-to-production gap in ML.

AWS ML Summit 2021 | Fireside Chat

Breakout sessions

The Summit included four audience-focused tracks which ran throughout the day:

  • Science of Machine Learning;
  • Impact of Machine Learning;
  • How Machine Learning Is Done; and
  • Machine Learning — No Experience Required

Science of Machine Learning track fireside chat

The Science of Machine Learning track comprised six 30-minute presentations, and a fireside chat on deep learning and language with Amazon distinguished scientists Alex Smola and Bernhard Schölkopf, and Alexa AI senior principal scientist Dilek Hakkani-Tur.

Machine Learning track fireside chat replay: Causality, robustness, and natural language understanding in ML

Science of Machine Learning track session videos and interviews

Below are video replays from each of the speakers in the Science of Machine Learning track, along with links to interviews Amazon Science did with each of the speakers in the weeks before the summit. Click here to see the full list of ML Summit videos.

Marzia Polito replay: Building high-quality computer vision models using only a few examples
AmazonScience_MLS-SpeakerGraphic_MarziaPolito_03_210505134738.jpg
3 questions with Marzia Polito: Performing computer vision tasks at scale with few-shot learning
Polito was one of the featured speakers at the first virtual Amazon Web Services Machine Learning Summit.
Read her interview

Michael Kearns replay: The ethical algorithm
AmazonScience_MLS-SpeakerGraphic_MichaelKearns_01_210505134739.jpg
3 questions with Michael Kearns: Designing socially aware algorithms and models
Kearns was a featured speaker at the first virtual Amazon Web Services Machine Learning Summit.
Read his interview

Philip Resnik replay: Analyzing social media for suicide risk using natural language processing
Philip Resnik, speaker at the ML Summit and a professor at the University of Maryland in the Department of Linguistics and at the Institute for Advanced Computer Studies
3 questions with Philip Resnik: Analyzing social media to understand the risks of suicide
Resnik was a featured speaker at the first virtual Amazon Web Services Machine Learning Summit.
Read his interview

Ryan Tibshirani replay: COVIDcast: An ecosystem for COVID-19 tracking and forecasting
Ryan Tibshirani, an associate professor of statistics and machine learning at Carnegie Mellon University, and an Amazon Scholar, is a co-principal investigator of the Delphi Group.
3 questions with Ryan Tibshirani: The science behind COVIDcast and pandemic tracking
Tibshirani was a featured speaker at the first virtual Amazon Web Services Machine Learning Summit.
Read his interview

Kathleen McKeown replay: Towards controllable language generation
ML-Summit-Image-KathleenMcKeown-2.png
Glynis Condon
3 questions with Kathleen McKeown: Controlling model hallucinations in natural language generation
McKeown was a featured speaker at the first virtual Amazon Web Services Machine Learning Summit.
Read her interview

George Karypis replay: Deep Graph Library: Deep Graph learning at scale
George Karypis
Glynis Condon
3 questions with George Karypis: Making learning from data embedded in graphs easy and scalable
Karypis was a featured speaker at the first virtual Amazon Web Services Machine Learning Summit.
Read his interview

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US, TX, Austin
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Applied Scientist, AWS Automated Reasoning
US, MA, Boston
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
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