Level: 300
The machine learning (ML) journey requires continuous experimentation and rapid prototyping to be successful. In order to create highly accurate models, data scientists have to first experiment with feature engineering, model selection, and optimization techniques, which can be time-consuming and expensive. In this session, learn how low-code ML tools, including Amazon SageMaker Data Wrangler, Amazon SageMaker Autopilot, and Amazon SageMaker JumpStart, make it easier to experiment faster and bring highly accurate models to production more quickly and efficiently.
Learning Objectives
- Discover how Amazon SageMaker Data Wrangler can help accelerate the time to prepare data for ML from weeks to minutes so you can build ML models faster.
- Learn how you can use Amazon SageMaker Autopilot to automatically create ML models with full visibility and eliminate the heavy lifting of building ML models.
- Understand how Amazon SageMaker JumpStart can help accelerate your ML journey with pretrained models and prebuilt solutions.
Who Should Attend?
Data Scientists, Data Engineers, ML Developers, ML Engineers
Speaker(s)
Claire O'Brien Rajkumar, Senior Product Manager, AWS; Charles Laughlin, Principal AI/ML SA, AWS
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/sagemaker/low-code/
Intro body copy here about 2018 re:Invent launches.
Download the slide deck
Compute
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.
Data Lakes & Analytics
Webinar 1:
What's New / Cloud Innovation
December 10th, 2018 | 11:00 AM PT
EMBARGOED
Register Now>