Amazon SageMaker: Simplifying Machine Learning Application Development from edX

What's inside

Learning objectives

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

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

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

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

Amazon SageMaker Algorithms

Introduction to Week 3

ML and Amazon SageMaker Terminology

SageMaker/ML Terminology and Algorithms

Hyperparameter Tuning

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)

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

Week 4 Notes and Resources

Week 4 Quiz

Class Wrap Up

Course Survey

Week 4 Discussion

End of Course Assessment (Verified Certificate Track Only)

Week 1

Week 2

Amazon SageMaker Notebooks and SDK

Week 3

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches skills, knowledge, and tools that are highly relevant to industry

Develops professional skills or deep expertise in a particular topic or set of topics

Takes a creative approach to an otherwise established topic, field, or subject

Taught by x, who are recognized for their work in x

May accelerate existing careers in ML or other fields with ML applications

Offers hands-on labs and interacive materials

Career center

Learners who complete Amazon SageMaker: Simplifying Machine Learning Application Development will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to uncover patterns and trends. They use statistical and machine learning techniques to build models that can predict future outcomes and solve business problems. This course provides a solid foundation in Machine Learning and its applications, which are essential skills for Data Scientists. By learning how to use Amazon SageMaker, you will gain the ability to develop and deploy ML models efficiently, making you a valuable asset to any organization that relies on data-driven decision-making.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying Machine Learning models. They work closely with Data Scientists to translate business problems into ML solutions. This course provides hands-on experience in using Amazon SageMaker to build and deploy ML models, which is a valuable skill for Machine Learning Engineers. By completing this course, you will be well-prepared to enter this rapidly growing field.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use statistical and visualization techniques to communicate their findings to stakeholders. This course provides a foundation in Machine Learning, which is increasingly being used by Data Analysts to automate data analysis tasks. By learning how to use Amazon SageMaker, you will gain the skills necessary to stay ahead of the curve and become a more valuable asset to your organization.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts are responsible for understanding the business needs of an organization and translating them into technical requirements. They work closely with stakeholders to gather requirements, analyze data, and develop solutions. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Business Analysts. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective partner to business stakeholders.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers are responsible for planning, executing, and delivering projects. They work with stakeholders to define project scope, develop timelines, and manage resources. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Project Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of technology projects.

See salaries and explore the career path for Project Manager

Software Developer

Software Developers are responsible for designing, developing, and testing software applications. They work with stakeholders to gather requirements, design solutions, and write code. This course provides hands-on experience in using Amazon SageMaker to build and deploy ML models, which is a valuable skill for Software Developers. By completing this course, you will be able to integrate ML into your software applications, making them more intelligent and efficient.

See salaries and explore the career path for Software Developer

Product Manager

Product Managers are responsible for defining the vision and roadmap for a product. They work with stakeholders to gather requirements, prioritize features, and launch the product. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Product Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of product development.

See salaries and explore the career path for Product Manager

IT Manager

IT Managers are responsible for planning, implementing, and managing IT systems. They work with stakeholders to define IT strategy, develop budgets, and manage vendors. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for IT Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of IT operations.

See salaries and explore the career path for IT Manager

Operations Manager

Operations Managers are responsible for planning, executing, and controlling the operations of an organization. They work with stakeholders to define operational goals, develop processes, and manage resources. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Operations Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of operational processes.

See salaries and explore the career path for Operations Manager

Marketing Manager

Marketing Managers are responsible for planning and executing marketing campaigns. They work with stakeholders to define marketing goals, develop strategies, and manage budgets. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Marketing Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of marketing campaigns.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for leading and motivating sales teams. They work with stakeholders to define sales goals, develop strategies, and manage budgets. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Sales Managers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective leader of sales teams.

See salaries and explore the career path for Sales Manager

Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations. They work with stakeholders to evaluate investment opportunities, develop financial models, and manage risk. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Financial Analysts. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective financial analyst.

See salaries and explore the career path for Financial Analyst

Consultant

Consultants are responsible for providing advice and guidance to organizations. They work with stakeholders to identify problems, develop solutions, and implement change. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Consultants. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective consultant.

See salaries and explore the career path for Consultant

Educator

Educators are responsible for teaching and training students. They work with students to develop knowledge and skills, and prepare them for success in the workforce. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Educators. By learning how to use Amazon SageMaker, you will gain the ability to integrate ML into your teaching, making you a more effective educator.

See salaries and explore the career path for Educator

Researcher

Researchers are responsible for conducting research and developing new knowledge. They work with stakeholders to define research questions, design studies, and analyze data. This course provides an overview of Machine Learning and its applications, which is becoming increasingly important for Researchers. By learning how to use Amazon SageMaker, you will gain the ability to evaluate and implement ML solutions, making you a more effective researcher.

See salaries and explore the career path for Researcher

Reading list

We haven't picked any books for this reading list yet.

Amazon SageMaker

Simplifying Machine Learning Application Development

What you'll learn

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Career center

Reading list

Share

Similar courses