Released
6/7/2019- Reviewing the crime-fighting case study
- Amazon SageMaker basics
- Preparing the data
- Training the model
- Evaluating the model
- Deploying a face-detection model to AWS DeepLens
- Retrieving data for the model with AWS Rekognition
- Sending data points to a SageMaker hosted model
- Retrieving predictions
- Making your models explainable
Skill Level Intermediate
Duration
Views
- [Kesha] Are you curious about machine learning? Maybe you've been wanting to learn about building models using Amazon SageMaker? Or maybe bias in AI is a huge concern for you, and you want to learn ways to mitigate it? If so, this course is for you. In this tactical hands-on course you will learn how to build a crime-fighting machine learning model that is fair, transparent, and explainable using Amazon SageMaker. You will also learn the steps to uncover and remove bias in training data before a model is created. You will learn all of this through a fun crime-fighting case study that integrates Amazon SageMaker, with Rekognition, and the AWS DeepLens camera. Creating a crime-fighting model that can see what's happening in a particular scene. Hello, I'm Kesha Williams. I'm a software engineering manager, author, and international speaker. I've been in the IT industry for over 20 years, and I believe that machine learning is the next wave of transformative technology that will change life in ways that we can't even begin to imagine. So let's get started learning how to build bias-free machine learning models using Amazon SageMaker, AWS DeepLens, and Rekognition.
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Video: Debiasing AI using Amazon SageMaker