Getting Started with Amazon SageMaker on AWS
Machine Learning is innovating quickly so now is the time to dive in. Fortunately, Amazon Web Services (AWS) makes this easy with a series of tools and services in their Artificial Intelligence and Machine Learning platform. Amazon SageMaker is the cornerstone of this platform and is a great way to understand the tools, technologies and concepts behind machine learning. Amazon SageMaker is built on the highly scalable and available Amazon Web Services (AWS) cloud platform. In this course, we will share the basic concepts of machine learning using Amazon SageMaker as the service of choice.
This 5-hour session will cover specific usage scenarios for machine learning so that you can recognize situations where machine learning makes sense. We will provide a high-level overview of the tools, languages and libraries that Amazon SageMaker uses, including the AWS console, Jupyter Notebooks and languages such as Python and interactive data analysis libraries such Pandas.
We’ll cover the components and architecture which comprise Amazon SageMaker. You’ll also walk away with an understanding of the common algorithms and models used with Machine Learning and Amazon SageMaker. This will help you determine the appropriate model to use for your business scenarios. We will also walk through Amazon SageMaker’s end to end workflow and process flow, by using sample business scenarios built into Amazon SageMaker.
You don’t need to be a data scientist or developer to benefit from this course. As much as it will be helpful to have some prior experience with AWS, knowing AWS isn’t required to take this course. If you have a programming background with software development and data analysis experience using languages such as Python and tools such as Jupyter notebooks, you’ll have a headstart. If you don’t we provide preliminary and follow-up courses you can take to supplement the knowledge in this training. AWS is continuing to make great strides to innovate their Artificial Intelligence and Machine Learning Platform. The concepts learned in this course will provide you with the foundation to build your own innovative systems on this dynamic platform.
What you'll learn-and how you can apply it
- Understand key machine learning concepts and taxonomy
- Be able to identify appropriate use cases and business scenarios than can benefit from Amazon SageMaker
- Learn about the languages, libraries and tools used in conjunction with Amazon SageMaker
- Learn how to identify, prepare, and load data for analysis with Amazon SageMaker
- Walk away with an understanding of Amazon SageMaker’s lifecycle end to end
- Build and train datasets, train and fine tune models, create predictions and deploy these models to production using Amazon SageMaker
- Receive a fundamental understanding of how to use Amazon SageMaker with the AWS Console & SageMaker Dashboard
- Walk away with a basic understanding of common tools used in Machine Learning such as Jupyter Notebooks
- Walk away with a preliminary understanding of the languages and libraries used with Machine Learning
- Understand how to learn from the business samples and models built into Amazon SageMaker
This training course is for you because...
- You have a passion for the latest advancements in machine learning
- You are a data scientist or developer who would like to build, train and deploy machine learning models with speed and ease
- You ideally have a background in software programming or data analysis and want to ensure you are current on the latest developments
- You want to learn the fundamentals of machine learning including concepts, taxonomy, workflow and tools
- You want to accelerate your learning of Amazon SageMaker by learning principles combined with pragmatic experience
- You enjoy interactive experience via labs and demonstrations
- An AWS Account, should you wish to follow along (AWS Free Tier can be used for 2 months).
Ideally, you have some experience with AWS, but it’s not required. For an introduction to cloud computing & AWS: - Amazon Web Services (AWS) LiveLessons, 2nd Edition By Richard A. Jones
Ideally, you should have familiarity software languages used with data analytics or software development preferred (e.g. Python, R); this course can be used to get up to speed on these topics: Data Analytics and Machine Learning LiveLessons
For those who are unfamiliar with the languages and libraries used in data analytics, these courses and books would be helpful: - For an introductory course on data programming, search the O'Reilly platform for the latest offering of this course: "Programming with Data: Foundations of Python and Pandas" - For a refresher of Panda and Python data analysis fundamentals, see Pandas Data Analysis with Python Fundamentals (video) and Pandas for Everyone: Python Data Analysis (book) - For applying machine learning with Jupyter Notebooks, see: Essential Machine Learning and AI with Python and Jupyter Notebook - For applying Python with Jupyter Notebooks, see: Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons
About your instructor
Asli Bilgin is an award-winning cloud computing executive who has over two decades of experience working for companies such as Dell, Microsoft and Amazon. Her firm, Nokta Consulting, specializes in IT transformation and modernization leveraging disruptive technologies such as cloud computing, machine learning and blockchain. At Amazon, Asli created, launched and ran the global Software as a Service program At Microsoft, she led the cloud and web strategy for 80 countries in the Middle East & Africa, based out of Dubai. Asli is a passionate advocate for the impact technology can make on people’s lives.. Asli was the architect behind the LEGO and Microsoft partnership effort for WomenBuild, a program to promote compute science as an art and science, specifically for girls and women.
The timeframes are only estimates and may vary according to how the class is progressing
Segment 1: Amazon Artificial Intelligence and Machine Learning Overview
- Evolution of Artificial Intelligence and Machine Learning
- What is Machine Learning (ML)?
- AWS ML & AI: Platform Services
- AWS ML & AI: Application Services
- AWS ML & AI: Foundational Services
Segment 2: How Does Amazon SageMaker Work?
(30 minutes) - What is Amazon SageMaker? - Who should use Amazon SageMaker? - What are the benefits of Amazon SageMaker? - High Level Overview - Options for Data Sources - Supervised Machine Learning - Unsupervised Machine Learning - Life Cycle of ML Processing - Pricing
Segment 3: Which Use Cases Can Amazon SageMaker Solve?
(30 minutes) - Personalization - Search - Marketing - Finance - Personal Productivity - Product Management
Break: 15 minutes
Segment 4: Interactive Lab: Amazon SageMaker Basics
(30 minutes) - Architecture Overview - Workflow - Introduction the Console - Amazon SageMaker Notebooks Service - Amazon SageMaker Training Service - Amazon SageMaker Hosting Service
Segment 5: Amazon SageMaker Ancillary Technologies
(20 minutes) - Jupyter Notebook Overview - Libraries and Languages - Containers and Instances - Choosing the Appropriate Technologies
Segment 6: Machine Learning Concepts and Taxonomy
(35 minutes) - Workflow- Build, Train Deploy - What are Features? - What is a Target? - What are Observations? - What is Labeled Data? - What is Unlabeled Data? - What is Ground Truth? - What is Input Data? - What are Hyperparameters? - Machine Learning Algorithm Categories - What are Predictions or Inferences?
Break: 15 minutes
Segment 7: A Closer Look at Models
(15 minutes) - High Level Review of Model Types - Common Model Types - Review Sample Models Provided by Amazon SageMaker
Segment 8: Choosing the Right DataSet and Model
(20 minutes) - Where can you get Sample Data? - Walkthrough of a SageMaker Sample Scenarios - Which Model Makes Sense for your Data?
Segment 9: Refining & Training a Model (15 minutes) - High Level Overview - Workflow - Best Practices
Break: 15 minutes
Segment 10: Deployment (15 minutes) - Deployment Overview and Options - Architectural Considerations - Sample Walkthrough
Segment 11: Call to Action & Key Takeaways
(15 minutes) - Summary - Next Steps - References