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In June 2022, Amazon re:MARS, the company’s in-person event that explores advancements and practical applications within machine learning, automation, robotics, and space (MARS), took place in Las Vegas. The event brought together thought leaders and technical experts building the future of artificial intelligence and machine learning, and included keynote talks, innovation spotlights, and a series of breakout-session talks.
Now, in our re:MARS revisited series, Amazon Science is taking a look back at some of the keynotes, and breakout session talks from the conference. We’ve asked presenters three questions about their talks, and provide the full video of their presentation.
On June 23, Gaurav Kaila, prototyping regional manager with Amazon Web Services (AWS), presented the talk, “Computer vision for automated quality inspection”. His session focused on how Baxter International, Inc. uses Amazon Lookout for Vision to build custom computer vision models to automate their quality inspection to detect defects in their manufacturing process.
What was the central theme of your presentation?
The presentation is focused around how to use machine learning to optimize and scale industrial operations at the edge, e.g., on the manufacturing floor. The session talks about different AWS technologies that are enabling our customers to build custom computer vision models to address business critical use-cases to drive operational efficiency.
In what applications do you expect this work to have the biggest impact?
Industrial applications, such as quality control, and predictive maintenance across a wide variety of industries including healthcare, manufacturing, and automotive.
What are the key points you hope audiences take away from your talk?
Machine learning is critical to the successful operation of our industrial customers. AWS provides both high-level and custom ML services that can help customers adopt ML out-of-the-box or develop their own set of models to address variety of use cases.
Amazon re:MARS 2022: Computer vision for automated quality inspection
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