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

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.
Course info
Level
Advanced
Updated
Jul 22, 2019
Duration
2h 41m
Table of contents
Course Overview
Getting Started with AWS SageMaker
Building Machine Learning Models Using AWS SageMaker
Training Machine Learning Models Using AWS SageMaker
Deploying Machine Learning Models Using AWS SageMaker
Managing Security and Scalability in AWS SageMaker
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
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.