Tom Diethe, applied science manager in Amazon AWS, discusses solutions for “technical debt” when deploying ML systems at the Bristol Data Science Seminar series. He covers engineering-based solutions available through AWS SageMaker cloud machine learning services that ensures ML systems are efficient in long-term usage, robust to changes in the environment, and potential errors are discoverable.

Learn more about Tom’s work at Amazon here: https://www.amazon.science/author/tom-diethe

Follow us:
Website: https://www.amazon.science
Twitter: https://twitter.com/AmazonScience
Facebook: https://www.facebook.com/AmazonScience
Instagram: https://www.instagram.com/AmazonScience
LinkedIn: https://www.linkedin.com/showcase/AmazonScience
Newsletter: https://www.amazon.science/newsletter

source