Course details

Enterprise Services: Enterprise Machine Learning with AWS

Enterprise Services: Enterprise Machine Learning with AWS


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.2

Lesson Objectives

Enterprise Services: Enterprise Machine Learning with AWS

  • 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
  • Course Number:
    it_mlmdesdj_01_enus

    Expertise Level
    Intermediate