Broadcast Date: February 28, 2019
Successful machine learning models are built on the foundation of large volumes of high-quality training data. But, the process to create the training data necessary to build these models is often expensive, complicated, and time-consuming. Amazon SageMaker Ground Truth significantly reduces the time and effort required to create datasets for training. These savings are achieved by using machine learning to automatically label data, and the model is able to get progressively better over time by continuously learning from labels created by human labelers. Join us and learn how companies like T-Mobile are using Amazon SageMaker Ground Truth to build highly accurate training datasets for machine learning quickly.
- Learn more about Amazon SageMaker Ground Truth and how to build training datasets with high accuracy and reduce costs by up to 70%
- Understand through a demo on how to get started with automated data labeling using machine learning
- Learn how customers are using Amazon SageMaker Ground Truth and their use cases
Who Should Attend?
Machine Learning Practitioners, Data Scientists, Developers, Data Architects, Technical Decision Makers
- Vikram Madan, Sr. Product Manager, AWS
- Heather Nolis, Machine Learning Engineer, T-Mobile
To learn more about the services featured in this talk, please visit:
Service How To
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