Free Download Build an AI Application with React and AWS SageMaker
Released: 04/2024
Duration: 1h 12m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 187 MB
Level: Intermediate | Genre: eLearning | Language: English
When you want a lot more control over your data, but don't want to manage machine learning tools locally, AWS SageMaker can provide the bridge you need. In this course, Emmanuel Henri shows you how to train SageMaker on your data, tune the results, and then connect it to a React-based application for delivery to your users. Emmanuel takes you through the process step by step, from the initial setup of the project, to feature engineering, training and deployment, and working with the React front end.
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Free Download Amazon SageMaker by Jose Portilla
Published 4/2024
Created by Jose Portilla
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 28 Lectures ( 4h 24m ) | Size: 1.83 GB
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Free Download Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE by Michael Hsieh
English | March 31, 2022 | ISBN: 1801070156 | 326 pages | MOBI | 17 Mb
Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code
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Free Download Debiasing AI Using Amazon SageMaker
Released: 06/2019
Duration: 1h 42m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 888 MB
Level: Intermediate | Genre: eLearning | Language: English
Artificial intelligence (AI) can have deeply embedded bias. It's the job of data scientists and developers to ensure their algorithms are fair, transparent, and explainable. This responsibility is critically important when building models that may determine policy—or shape the course of people's lives. In this course, award-winning software engineer Kesha Williams explains how to debias AI with Amazon SageMaker. She shows how to use SageMaker to create a predictive-policing machine-learning model that integrates Rekognition and AWS DeepLens, creating a crime-fighting model that can "see" what's happening in a live scene. By following the development process, you can learn what goes into making a model that doesn't suffer from cultural prejudices. Kesha also discusses how to remove bias in training data, test a model for fairness, and build trust in AI by making models that are explainable.
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