PLU | Build, Train, And Deploy Machine Learning Models With AWS SageMaker (2019 EN)

Company: Pluralsight
Author: Jorge Vasquez
Full Title: Build, Train, And Deploy Machine Learning Models With AWS SageMaker
Year: 2019
Language: English
Genre: Educational: Application Development
Skill Level: Advanced
Price: -
-
Files: MP4 (+ Exercise Files, Slides .PDF)
Time: 01:15:32
Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 350 kbps
Audio: AAC at 96 Kbps, 2 channels, 44.1 KHz



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.

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.


Lessons:
1. Course Overview
01. Course Overview
2. Getting Started with AWS SageMaker
02. Introduction
03. Course Scenario
04. Overview of How the Sample REST API for Breast Cancer Detection Should Work
05. Introduction to AWS SageMaker
06. Setting up AWS SageMaker
07. Summary
3. Building Machine Learning Models Using AWS SageMaker
08. Introduction
09. SageMaker Notebook Instances
10. Creating a Notebook Instance
11. Overview of the Image Classification Built-in Algorithm
12. Obtaining, Exploring, and Preprocessing Histopathology Images
13. Configuring the Image Classification Algorithm Using the Low-level AWS SDK for Python
14. Configuring the Image Classification Algorithm Using the High-level SageMaker Python Library
15. Overview of Using Tensorflow in SageMaker
16. Converting Images to the TFRecord Format
17. Configuring a Tensorflow Estimator Using the High-level SageMaker Python Library
18. Overview of Using Apache MXNet in SageMaker
19. Configuring a MXNet Estimator Using the High-level SageMaker Python Library
20. Summary
4. Training Machine Learning Models Using AWS SageMaker
21. Introduction
22. Overview of Creating Training Jobs in SageMaker
23. Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python
24. Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library
25. Creating and Monitoring a Training Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library
26. Creating and Monitoring a Training Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library
27. Overview of Automatic Hyperparameter Optimization
28. Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python
29. Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library
30. Creating and Monitoring a Tuning Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library
31. Creating and Monitoring a Tuning Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library
32. Summary
5. Deploying Machine Learning Models Using AWS SageMaker
33. Introduction
34. Overview of Deploying and Testing Machine Learning Models in AWS SageMaker Hosting Services
35. Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python
36. Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library
37. Deploying and Testing the Trained Model Based on a Custom Tensorflow Algorithm Using the High-level SageMaker Python Library
38. Deploying and Testing the Trained Model Based on a Custom Mxnet Algorithm Using the High-level SageMaker Python Library
39. Overview of Integrating Endpoints with AWS API Gateway and AWS Lambda
40. Integrating an AWS SageMaker Endpoint with AWS API Gateway and AWS Lambda
41. Summary
6. Managing Security and Scalability in AWS SageMaker
42. Introduction
43. Overview of Managing Authentication and Access Control Using IAM Policies
44. Configuring Access Control to Notebook Instances
45. Overview of Monitoring and Troubleshooting Deployed Models with AWS CloudWatch
46. Analyzing Endpoint Metrics and Logs with AWS CloudWatch
47. Overview of Configuring Automatic Scaling for AWS SageMaker Endpoints
48. Configuring Automatic Scaling for an AWS SageMaker Endpoint Using the AWS Console
49. Summary

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