Key Information
About the content
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.
*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.
- 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
Prerequisite
- Prior application development experience
- Experience with the AWS Console
- Recommended: AWS Developer Professional Series (Building on AWS, Deploying on AWS, Optimizing on AWS)
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
- 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)
Instructors
Russell Sayers
Senior Cloud Technologist
Amazon Web Services
Asim Jalis
Senior Technical Trainer
Amazon Web Services
Carl Leonard
Technical Trainer
Amazon Web Services
Content Designer
Platform
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