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

Discussion in 'Artificial Intelligence' started by Kanka, Jul 28, 2019.

  1. Kanka

    Kanka Well-Known Member Loyal User

    Messages:
    14,444
    Likes Received:
    384
    Trophy Points:
    83
    [​IMG]

    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


    -------------