Build, Train, and Deploy Machine Learning Models with Amazon SageMaker 1

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Jorge Vasquez

2:40:12

34 View

00. Course Overview

00. Course Overview.mp4

01:29

01. Getting Started with AWS SageMaker

00. Introduction.mp4

00:44

01. Course Scenario.mp4

00:52

02. Overview of How the Sample REST API for Breast Cancer Detection Should Work.mp4

01:36

03. Introduction to AWS SageMaker.mp4

04:25

04. Setting up AWS SageMaker.mp4

03:57

05. Summary.mp4

01:02

02. Building Machine Learning Models Using AWS SageMaker

00. Introduction.mp4

00:32

01. SageMaker Notebook Instances.mp4

01:10

02. Creating a Notebook Instance.mp4

02:56

03. Overview of the Image Classification Built-in Algorithm.mp4

03:08

04. Obtaining, Exploring, and Preprocessing Histopathology Images.mp4

09:55

05. Configuring the Image Classification Algorithm Using the Low-level AWS SDK for Python.mp4

08:09

06. Configuring the Image Classification Algorithm Using the High-level SageMaker Python Library.mp4

02:05

07. Overview of Using Tensorflow in SageMaker.mp4

02:29

08. Converting Images to the TFRecord Format.mp4

03:53

09. Configuring a Tensorflow Estimator Using the High-level SageMaker Python Library.mp4

09:29

10. Overview of Using Apache MXNet in SageMaker.mp4

01:30

11. Configuring a MXNet Estimator Using the High-level SageMaker Python Library.mp4

09:50

12. Summary.mp4

01:00

03. Training Machine Learning Models Using AWS SageMaker

00. Introduction.mp4

00:36

01. Overview of Creating Training Jobs in SageMaker.mp4

03:21

2.mp4

06:22

3.mp4

03:36

4.mp4

03:36

5.mp4

03:26

06. Overview of Automatic Hyperparameter Optimization.mp4

01:45

7.mp4

08:58

8.mp4

03:24

9.mp4

03:36

10.mp4

03:21

11. Summary.mp4

00:44

04. Deploying Machine Learning Models Using AWS SageMaker

00. Introduction.mp4

01:01

01. Overview of Deploying and Testing Machine Learning Models in AWS SageMaker Hosting Services.mp4

01:24

2.mp4

06:43

3.mp4

03:44

4.mp4

03:48

5.mp4

03:36

06. Overview of Integrating Endpoints with AWS API Gateway and AWS Lambda.mp4

02:05

07. Integrating an AWS SageMaker Endpoint with AWS API Gateway and AWS Lambda.mp4

06:06

08. Summary.mp4

00:42

05. Managing Security and Scalability in AWS SageMaker

00. Introduction.mp4

00:39

01. Overview of Managing Authentication and Access Control Using IAM Policies.mp4

01:38

02. Configuring Access Control to Notebook Instances.mp4

08:02

03. Overview of Monitoring and Troubleshooting Deployed Models with AWS CloudWatch.mp4

01:51

04. Analyzing Endpoint Metrics and Logs with AWS CloudWatch.mp4

02:05

05. Overview of Configuring Automatic Scaling for AWS SageMaker Endpoints.mp4

00:56

06. Configuring Automatic Scaling for an AWS SageMaker Endpoint Using the AWS Console.mp4

02:04

07. Summary.mp4

00:52

Description

In this course, you are going to learn the skills you need to build, train, and deploy machine learning models in Amazon SageMaker, including how to create REST APIs to integrate them into your applications for solving real-world problems.

What You'll Learn?

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 Amazon SageMaker, you will gain the ability to create machine learning models in Amazon 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 Amazon SageMaker. When you’re finished with this course, you will have a foundational understanding of Amazon SageMaker that will help you immensely as you move forward to create your own machine-learning-enabled applications applied to different real-life scenarios.

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Machine Learning

Jorge Vasquez

Instructor's Courses

Jorge is a passionate person who loves building quality software that allows people to solve their problems. He also loves to teach, that's why he works several years ago teaching programming and software development to university students. He has experience developing highly performant backend systems with Java and Node.js, building ETL processes with Python and Scala and working with cloud platforms such as Amazon Web Services. His current areas of interest include Deep Neural Networks, Computer Vision, Natural Language Processing and Reinforcement Learning. When not developing software or teaching, he enjoys photography and spending time with his family.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.