AWS Certified Machine Learning: Model Training & Evaluation

Amazon Web Services    |    Intermediate
  • 12 videos | 30m 54s
  • Includes Assessment
  • Earns a Badge
Rating 3.1 of 16 users Rating 3.1 of 16 users (16)
Training a machine learning (ML) model is the first step of many when developing ML applications that enable businesses to discover new trends within broad and diverse data sets. Use this course to learn more about SageMaker's built-in algorithm and perform model training, evaluation, monitoring, tuning, and deployment using Amazon Elastic Compute Cloud (EC2) instances. Begin by examining factorization machines and the selection of EC2 instances. Next, you'll discover how to perform model training, evaluation, and deployment. You'll wrap up the course by exploring the steps involved in tuning and testing ML models. After you're done with this course, you'll have the skills and knowledge to successfully train and evaluate a model, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe how factorization machines work and specify why they are powerful tools for recommender systems
    List and describe ec2 instances that can be used with sagemaker
    Work with training recommender system on amazon reviews dataset using python and sagemaker
    Demonstrate how to reduce cost while training machine learning algorithms using spot instances
    Evaluate a trained machine learning algorithm
  • Deploy a machine learning model using api endpoints
    Monitor api usage in real-time
    Work with feature engineering and machine learning experimentations using python and sagemaker
    Demonstrate how to run hyperparameter tuning jobs with sagemaker using python and amazon reviews dataset
    Conduct a/b testing for models trained on amazon reviews dataset using production variants
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 53s
  • 2m 54s
    After completing this video, you will be able to describe how factorization machines work and why they are powerful tools for recommender systems. FREE ACCESS
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    3.  EC2 Instances
    3m 15s
    In this video, you will learn how to list and describe EC2 instances that can be used with SageMaker. FREE ACCESS
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    4.  Training with EC2 Instances
    3m 14s
    Upon completion of this video, you will be able to work with a training recommender system on the Amazon Reviews dataset using Python and SageMaker. FREE ACCESS
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    5.  Training with EC2 Spot Instances
    1m 24s
    During this video, you will learn how to reduce cost while training machine learning algorithms using Spot instances. FREE ACCESS
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    6.  Evaluating Machine Learning Models
    1m 1s
    To evaluate a trained machine learning algorithm, you can use a variety of methods, including accuracy, precision, recall, and F1 score. FREE ACCESS
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    7.  Deploying Machine Learning Models
    6m 19s
    Learn how to deploy a machine learning model using API endpoints. FREE ACCESS
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    8.  Monitoring Machine Learning Models
    1m 55s
    In this video, you will learn how to monitor API usage in real time. FREE ACCESS
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    9.  Working with Feature Engineering
    3m 38s
    During this video, you will discover how to work with feature engineering and machine learning experimentations using Python and SageMaker. FREE ACCESS
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    10.  Performing Hyperparameter Tuning
    3m 19s
    In this video, discover how to apply hyperparameter tuning jobs with SageMaker using Python and the Amazon Reviews dataset. FREE ACCESS
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    11.  Using Production Variants
    2m 28s
    Discover how to conduct A/B testing for models trained on the Amazon Reviews dataset using production variants. FREE ACCESS
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    12.  Course Summary
    34s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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