Extending Amazon Machine Learning

Amazon ML 2020    |    Expert
  • 15 videos | 1h 1m 18s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 11 users Rating 4.0 of 11 users (11)
The Amazon Machine Learning framework allows you to quickly deploy machine learning models using Amazon Web Services, automate model deployment and maintenance, and configure other Amazon tools to work in synchronicity. AI practitioners should consider the benefits and best practices of working with Amazon ML and other Amazon services in their AI development projects. In this course, you'll explore advanced techniques for working with the Amazon ML framework. You'll examine the significant differences between Amazon ML and other frameworks. You'll recognize the advantages of using the Amazon ML platform for certain projects and identify the Amazon ML workflow. Finally, you'll complete a project developing and training an AI model using the Amazon ML framework, and troubleshoot typical problems that come up during model training and evaluation.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Specify cases in which it is advantageous to use amazon ml over other platforms
    Compare the use of amazon ml and google cloud platform
    Compare the use of amazon ml and azure ml
    Identify possible data sources for working with amazon ml
    Describe the capabilities of amazon ml in relation to feature processing
    Specify multiple approaches to how data can be split using amazon ml
    List model types present in amazon ml and specify their purposes
  • Describe the process of batch prediction in amazon ml and identify cases in which batch prediction is more desirable than online prediction
    Describe how real-time prediction is made in amazon ml
    Troubleshoot common problems and identify approaches to improve model accuracy in amazon ml
    Describe sagemaker requirements and problem to be solved
    Demonstrate sagemaker model training and validation
    Validate the results of the sagemaker model demo
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 3m 4s
  • 3m 9s
    Upon completion of this video, you will be able to specify cases in which it is advantageous to use Amazon ML over other platforms. FREE ACCESS
  • Locked
    3.  Amazon ML vs. Google Cloud Platform
    2m 48s
    During this video, you will learn how to compare the use of Amazon ML and Google Cloud Platform. FREE ACCESS
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    4.  Amazon ML vs. Azure ML
    3m 8s
    In this video, learn how to compare the use of Amazon Machine Learning and Azure Machine Learning. FREE ACCESS
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    5.  Data Sources in Amazon ML
    4m 10s
    In this video, you will learn how to identify possible data sources for working with Amazon ML. FREE ACCESS
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    6.  Feature Processing in Amazon ML
    3m 13s
    After completing this video, you will be able to describe the capabilities of Amazon ML in relation to feature processing. FREE ACCESS
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    7.  Data Splitting in Amazon ML
    4m 7s
    Upon completion of this video, you will be able to specify multiple approaches to splitting data using Amazon ML. FREE ACCESS
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    8.  Amazon ML Model Types
    2m 47s
    After completing this video, you will be able to list the model types present in Amazon ML and specify their purposes. FREE ACCESS
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    9.  Batch Prediction in Amazon ML
    4m 36s
    After completing this video, you will be able to describe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online prediction. FREE ACCESS
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    10.  Real-time Prediction in Amazon ML
    5m 17s
    Upon completion of this video, you will be able to describe how Amazon ML makes real-time predictions. FREE ACCESS
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    11.  Improving Model Accuracy in Amazon ML
    3m 45s
    In this video, you will learn how to troubleshoot common problems and identify approaches to improve model accuracy in Amazon ML. FREE ACCESS
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    12.  Introduction to Sagemaker
    8m 9s
    Upon completion of this video, you will be able to describe the requirements for Sagemaker and the problem to be solved. FREE ACCESS
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    13.  Sagemaker Model Training and Validation
    10m 14s
    During this video, you will learn how to train and validate a Sagemaker model. FREE ACCESS
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    14.  Validating Sagemaker Model Results
    2m 4s
    Find out how to validate the results of the Sagemaker model demo. FREE ACCESS
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    15.  Course Summary
    49s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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