Learn to integrate Machine Learning into your apps with training from AWS experts--and without a data science background. Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.
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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.
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.
What you'll learn
- 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
Syllabus
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
Weekly Quiz, 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)
Prerequisites:
Prior application development experience
Experience with the AWS Console
Recommended: AWS Developer Professional Series , (Building on AWS, Deploying on AWS, Optimizing on AWS)
