Enterprise Services: Enterprise Machine Learning with AWS

Machine Learning    |    Intermediate
  • 15 Videos | 1h 19m 33s
  • Includes Assessment
  • Earns a Badge
Likes 13 Likes 13
This course explores features and operational benefits of using a cloud platform to implement machine learning (ML). In this 15-video course, learners observe how to manage diversified kinds of data, and the exponential growth of unstructured and structured data. First, you will examine ML workflow and compare differences between ML model development and traditional enterprise software development. Then you will learn how to use the ML services provided by AWS (Amazon Web Services) to implement end-to-end ML solutions at scale. Next, learners will examine AWS ML tools, services, and capabilities, the architecture, and internal components in Amazon SageMaker. You will continue by learning how to use Amazon Machine Learning Console to create data sources, implement ML models, and to use the models to facilitate predictions. This course compares the ML implementation scenarios and solutions in AWS, Microsoft Azure, and Google Cloud, and helps learners identify the best fit for any given scenario. Finally, you learn to use SageMaker and SageMaker Neo to create, train, tune, and deploy ML models anywhere.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize cloud features that provide significant operational benefits for implementing machine learning
    describe the machine learning workflow and differentiate between machine learning model development and traditional enterprise software development
    recall the machine learning tools, services, and capabilities provided by AWS
    compare machine learning implementation scenarios and solutions in AWS, Microsoft Azure, and Google Cloud to be able to identify the best fit for any given scenario
    describe the machine learning objects and the mechanisms of generating and interpreting predictions available with AWS
    use the Amazon Machine Learning console to create data sources and build machine learning models, and use the models to generate predictions
    describe the architecture of Amazon SageMaker as well as the internal AWS components used in Amazon SageMaker with focus on algorithm, training, and hosting services
  • use the Amazon SageMaker to create, train, and deploy simple machine learning models
    describe the features of Lex, Polly, and Transcribe and their roles in machine learning implementation
    recognize the features of Amazon SageMaker Neo that enable machine learning models to train once and run anywhere
    use Augmented Manifest to train object detection machine learning model with Amazon SageMaker
    describe the automatic model tuning capabilities of Amazon SageMaker that are applied for hyperparameter tuning functionality
    use Amazon SageMaker for hyperparameter tuning and use the pre-built TensorFlow container and MNIST dataset to tune models
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m
    UP NEXT
  • Playable
    2. 
    Cloud and Machine Learning
    5m 8s
  • Locked
    3. 
    Machine Learning Workflow Comparison
    5m 40s
  • Locked
    4. 
    AWS Machine Learning Tools and Capabilities
    6m 22s
  • Locked
    5. 
    Cloud Machine Learning Implementation Comparison
    4m 57s
  • Locked
    6. 
    Generating Machine Learning Objects and Prediction
    6m 2s
  • Locked
    7. 
    Amazon Machine Learning Console
    3m 47s
  • Locked
    8. 
    Amazon SageMaker Architecture
    5m 41s
  • Locked
    9. 
    Using Amazon SageMaker
    8m 2s
  • Locked
    10. 
    Lex, Polly, and Transcribe
    4m 31s
  • Locked
    11. 
    Amazon SageMaker Neo
    2m 32s
  • Locked
    12. 
    Augmented Manifest in Amazon SageMaker
    4m 7s
  • Locked
    13. 
    Amazon SageMaker Model Tuning
    2m 33s
  • Locked
    14. 
    Amazon SageMaker Automatic Tuning
    11m 20s
  • Locked
    15. 
    Course Summary
    1m 21s

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