Amazon SageMaker:简化机器学习应用程序开发

Amazon SageMaker: Simplifying Machine Learning Application Development

Learn to integrate Machine Learning into your apps with training from AWS experts–and without a data science background.

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  • 完成时间大约为 4
  • 中级
  • 英语


Key problems that Machine Learning can address and ultimately help solve

How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance

How to publish a model using Amazon SageMaker

How to integrate the published SageMaker endpoint with an application


Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.
This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.
Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises* as we explore this complex topic from the lens of the application developer.
Added Bonus: Students who enroll in this course as a verified learner and complete this course by May 31, 2020 become eligible to earn a voucher for the AWS Certified Machine Learning – Specialty practice exam. This practice exam voucher will allow you to test your Machine Learning skills in an online exam environment and will review some of the subjects covered in this course. At a $40 value, the practice exam voucher is available only to students who earn a Verified Certificate through edX.
*Note that there may be a cost associated with some exercises. If you do not wish to incur additional expenses, you may view demonstrations instead.


Welcome to Machine Learning with Amazon SageMaker

Course Introduction
Welcome to Machine Learning with SageMaker on AWS
Course Welcome and Student Information
Meet the Instructors
Introduce Yourself

Week 1

Introduction to Machine Learning with SageMaker on AWS
Introduction to Week 1
What we we use ML for?
Diving Right In
What is Amazon SageMaker

WeeklyQuiz, Readings, Resources, Discussion
Week 1 Notes and Resources
Week 1 Quiz
Week 1 Discussion

Week 2

Amazon SageMaker Notebooks and SDK

Introduction to Week 2

Amazon SageMaker Notebooks
Introduction to Jupyter Notebooks
Notebooks and Libraries: Cleaning and Preparing Data
Exercise 2.1 Walkthrough
Exercise 2.1: Create Your Notebook Instance (Optional)

Weekly Quiz, Readings, Resources, Discussion
Week 2 Notes and Resources
Week 2 Quiz
Week 2 Discussion

Week 3

Amazon SageMaker Algorithms
Introduction to Week 3

ML and Amazon SageMaker Terminology
SageMaker/ML Terminology and Algorithms
Hyperparameter Tuning

Amazon SageMaker Algorithms
k-means Algorithm Walkthrough
Introduction to Exercise 3.1
Exercise 3.1: Using the k-means Algorithm (Optional)
XGBoost Algorithm Walkthrough (Part 1)
XGBoost Algorithm Walkthrough (Part 2)
XGBoost Algorithm Walkthrough (Part 3)
Introduction to Exercise 3.2
Exercise 3.2: Using the XGBoost Algorithm (Optional)

Weekly Quiz, Readings, Resources, Discussion
Week 3 Notes and Resources
Week 3 Quiz
Week 3 Discussion

Week 4

Application Integration
Introduction to Week 4

Integrating Amazon SageMaker with your Applications
Serverless Recap
Exercise 4.1 Walkthrough
Exercise 4.1: Python Movie Recommender (Optional)
Bring Your Own Models
Bringing Your Own Models: MXNet and TensorFlow

Weekly Quiz, Readings, Resources, Discussion
Week 4 Notes and Resources
Week 4 Quiz
Class Wrap Up
Course Survey
Week 4 Discussion

End of Course Assessment (Verified Certificate Track Only)


Prior application development experience
Experience with the AWS Console
Recommended: AWS Developer Professional Series (Building on AWS, Deploying on AWS, Optimizing on AWS)


Q. Are there any prerequisites for this course?
A. We recommend having at least one year of software development experience, and a basic understanding of AWS services and the AWS console, either through previous experience or the AWS Professional Developer Series on edX.
Q. Is it a requirement that I complete the AWS Professional Developer Series on edX before taking this course?
A. No this is not a requirement. However, this course assumes some understanding of several AWS services and the AWS console. If you do not have this experience, it may be beneficial for you to take at least one course from the AWS Professional Developer Series.
Q. Are there any costs associated with this course?
A. Learners can register for the course in an Audit track or Verified Certificate track. The Audit track is free, but has restrictions. The Verified Certificate track costs $99 and provides full access to course content, including graded assessments and assignments, for the duration. Please visit for more information.
For a limited time, individuals on the Verified Certificate track have a chance to earn an added bonus - an AWS exam voucher the for the “AWS Certified Machine Learning - Specialty” practice exam. This AWS practice exam is an excellent way for individuals to test their knowledge in Machine Learning and prepare to take a full exam to receive an AWS Certification. Only edX learners who complete the course and submit their certificate to AWS by May 31, 2020 may receive an AWS practice exam voucher code. edX learners must pass the course and email a copy of their certificate to
In addition to course registration costs, this course provides optional hands-on exercises which will have an associated charge in your AWS account. The AWS Free Tier provides access to SageMaker for two months after account sign-up. Please familiarize yourself with Amazon SageMaker Pricing at, and the AWS Free Tier at
Please note that the AWS Free Tier also has a limit on the amount of resources that you can consume before you begin accruing charges. If you perform these hands-on exercises, there is a chance you may incur charges on your AWS account. Please visit the AWS Free Tier page for more information.
Q. How much time will this course require?
A. If following the weekly schedule, learners should plan to spend 2-4 hours per week on this course. However, learners may complete the course at their own pace..
Q. Will I receive a certificate for this course?
A. Learners enrolled in the Verified Certificate path will receive a certificate upon successful completion of the course. And for a limited time, these same learners have the chance to earn an AWS exam voucher the for the “AWS Certified Machine Learning - Specialty” practice exam. edX learners must pass the course and email a copy of their certificate to
Q. What is the grading policy for this course?
A. All learners may take weekly quizzes, which are not graded and allow unlimited retries.
Learners in the Verified Certificate track are able to take the final course assessment in the course. The final assessment does not allow retries and requires a score of 65% or better to pass. Passing the final assessment is required to obtain the Verified Certificate.
Learners in the Audit track will not have access to the final assessment, and will not be able to earn a certificate.
Q. How are discussions used in this course?
A. This course has discussion groups aligned to each week of the course. We encourage learners to ask questions or offer suggestions and feedback. AWS Instructors will monitor the discussion groups to answer questions specific to the exercises and topics covered in the course.
Q. When will course content be available?
A. All course content will be available when the course opens on December 14, 2018. Since AWS frequently publishes service updates and new features/functionality, there may be a need to update the course content during its lifetime. If course content is updated, a notice will be placed on the course home page.
Q. Will this course help me prepare for an AWS Certification?
A. Earning an AWS Certification typically requires both knowledge and experience. While this course, if taken in isolation, will provide you with baseline information about Machine Learning and Amazon SageMaker, it likely will not equip you to earn an AWS Certification. For more information about AWS Certifications, including recommended training and experience requirements, visit
Q: What is an AWS practice exam?
A: An AWS practice exam is designed to help individuals prepare for AWS Certification exams. An AWS Certification builds your credibility and confidence by validating your cloud expertise with an industry-recognized credential. These practice exams help you test your knowledge before taking the actual Certification exam.
Q: How do I earn my practice exam voucher for the AWS Certified Machine Learning practice exam?
A: To qualify for and receive your practice exam voucher, you need to:

Enroll and verify in the Amazon SageMaker: Simplifying Machine Learning Application Development course on edX as a verified learner
Complete and pass the course by May 31, 2020
Send an email to requesting your voucher code and attach your edX course completion certificate by May 31, 2020

Q: Will my practice exam voucher code expire?
A: Yes, you will need to redeem your voucher code by August 31, 2020. If you try to redeem your voucher code after this date, the code will be invalid.

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