AI and machine learning at Amazon
summary
Amazon describes two production wins: Amazon Go checkout-less stores use computer vision and sensor fusion to let customers walk out and get charged by push notification, and Alexa voice service runs NLU at low latency for real-time voice interaction. For builders, AWS pushes Amazon SageMaker as an end‑to‑end platform with notebook-to-cluster parity, streaming/incremental training and scalable distributed jobs. Claims include tens of thousands of external customers on AWS ML and a focus on iteration, deployment elasticity and failure-tolerant production models.
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chapters (8)
- 0:00Start
- 2:21It's never been easier or cheaper to collect data at any scale
- 3:21Three trends we're seeing as customers adopt ML
- 4:23Build, train, tune, and host custom models with Amazon Sage Maker
- 4:38Rethinking Algorithms Design for Infinite Scale and Streaming Data
- 5:10Developers love speed in Al experimentation
- 5:22Even faster iterative machine learning with Sage Maker local mode
- 6:06Tens of thousands of customers running Machine Learning on AWS
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