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

By Jorge Vasquez
In this course, you are going to learn the skills you need to build, train, and deploy machine learning models in AWS SageMaker, including how to create REST APIs to integrate them into your applications for solving real-world problems.
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Course info
Level
Advanced
Updated
Jul 22, 2019
Duration
2h 41m
Table of contents
Course Overview
Course Overview 1m
Getting Started with AWS SageMaker
Introduction 1m Course Scenario 1m Overview of How the Sample REST API for Breast Cancer Detection Should Work 2m Introduction to AWS SageMaker 4m Setting up AWS SageMaker 4m Summary 1m
Building Machine Learning Models Using AWS SageMaker
Introduction 1m SageMaker Notebook Instances 1m Creating a Notebook Instance 3m Overview of the Image Classification Built-in Algorithm 3m Obtaining, Exploring, and Preprocessing Histopathology Images 10m Configuring the Image Classification Algorithm Using the Low-level AWS SDK for Python 8m Configuring the Image Classification Algorithm Using the High-level SageMaker Python Library 2m Overview of Using Tensorflow in SageMaker 2m Converting Images to the TFRecord Format 4m Configuring a Tensorflow Estimator Using the High-level SageMaker Python Library 9m Overview of Using Apache MXNet in SageMaker 2m Configuring a MXNet Estimator Using the High-level SageMaker Python Library 10m Summary 1m
Training Machine Learning Models Using AWS SageMaker
Introduction 1m Overview of Creating Training Jobs in SageMaker 3m Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 6m Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 4m Creating and Monitoring a Training Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m Creating and Monitoring a Training Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library 3m Overview of Automatic Hyperparameter Optimization 2m Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 9m Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 3m Creating and Monitoring a Tuning Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m Creating and Monitoring a Tuning Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library 3m Summary 1m
Deploying Machine Learning Models Using AWS SageMaker
Introduction 1m Overview of Deploying and Testing Machine Learning Models in AWS SageMaker Hosting Services 1m Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 7m Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 4m Deploying and Testing the Trained Model Based on a Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m Deploying and Testing the Trained Model Based on a Custom Mxnet Algorithm Using the High-level SageMaker Python Library 4m Overview of Integrating Endpoints with AWS API Gateway and AWS Lambda 2m Integrating an AWS SageMaker Endpoint with AWS API Gateway and AWS Lambda 6m Summary 1m
Managing Security and Scalability in AWS SageMaker
Introduction 1m Overview of Managing Authentication and Access Control Using IAM Policies 2m Configuring Access Control to Notebook Instances 8m Overview of Monitoring and Troubleshooting Deployed Models with AWS CloudWatch 2m Analyzing Endpoint Metrics and Logs with AWS CloudWatch 2m Overview of Configuring Automatic Scaling for AWS SageMaker Endpoints 1m Configuring Automatic Scaling for an AWS SageMaker Endpoint Using the AWS Console 2m Summary 1m
Description
Course info
Level
Advanced
Updated
Jul 22, 2019
Duration
2h 41m
Description

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.

About the author
Jorge Vasquez
About the author

Jorge is a software developer and university teacher. He has experience developing backend systems with Java and Node.js, building ETL processes with Python and Scala and working with cloud platforms such as AWS.

Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Jorge Vasquez, and welcome to my course, Build, Train, and Deploy Machine Learning Models with AWS SageMaker. AWS SageMaker is a fully-managed machine learning service and it's a great place to start if you want to quickly get the machine learning into your applications. In this course, you are going to learn the skills you need to create the machine learning models in AWS SageMaker and to iterate them into your applications. Some of the major topics that we will cover include building and training machine learning models in AWS SageMaker, deploying trained models to AWS SageMaker hosting services, building REST APIs for integrating deployed models with external applications using AWS API gateway and AWS lambda, managing security and the scalability in AWS SageMaker. By the end of this course, you'll be ready to create the machine learning models in AWS SageMaker for your own use cases so you can integrate them with your own applications. Before beginning the course, you should be familiar with machine learning basic concepts, deep learning and convolutional neural networks concepts, Python 3 programming, using Jupyter notebooks, using TensorFlow, and Apache MXNet. I hope you'll join me on this journey to learn AWS SageMaker with the Build, Train, and Deploy Machine Learning Models with AWS SageMaker course, at Pluralsight.

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