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Build, Train, and Deploy Machine Learning Models with AWS SageMaker

Build, Train, and Deploy Machine Learning Models with AWS SageMaker

with Jorge Vasquez


025-Creating and Monitoring a Training Job for the Custom Tensorflow Algorithm Using the High level SageMaker Python Library



A fully managed machine learning service is a great place to start if you want to quickly get machine learning into your applications. In this course, Build, Train, and Deploy Machine Learning Models with AWS SageMaker, you will gain the ability to create machine learning models in AWS SageMaker and to integrate them into your applications. First, you'll learn the basics and how to set up SageMaker. Next, you'll discover how to build, train, and deploy models applied to Image Classification for breast cancer detection and how to integrate them into a REST API. Finally, you will even discover how to manage security and scalability in AWS SageMaker. When you're finished with this course, you will have a foundational understanding of AWS SageMaker that will help you immensely as you move forward to create your own machine-learning-enabled applications applied to different real-life scenarios.

      
Course Contents
01 Course Reviews 02 Getting Started with AWS Sagemaker 03 Construction of Machine Learning Models with AWS Sagemaker 04 Teaching Machine Learning Models with AWS Sagemaker 05 Deployment of Machine Learning Models with AWS Sagemaker 06 Security Management and Scalability in AWS Sagemaker Exercise Files

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