Using Amazon SageMaker to develop and deploy ML models
summary
Amazon SageMaker provides a full ML pipeline: data prep in Jupyter, scalable training on p3 GPU instances and the Deep Learning AMI, and production inference with container-based training and inference that can run on edge via Greengrass. The service ships over 14 built-in algorithms, accepts custom Docker training containers, and exports inference containers for on‑prem use. Examples cited include Rekognition for images/video, Polly voices in 27 languages, Translate for on‑demand review translation, and Amazon Go vision systems.
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chapters (7)
- 0:00Introduction
- 0:25How does the cloud enable innovation
- 3:33How does AWS differentiate
- 8:59How does AWS accelerate innovation
- 10:51How does SageMaker work
- 12:59Use cases
- 16:17Service development
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