Practical Data Science with Amazon SageMaker

Practical Data Science with Amazon SageMaker Course Overview

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.

• Course level: Intermediate

• Duration: 1 day

• Activities

• This course includes presentations, hands-on labs, and demonstrations.

Skills Gained

In this course, you will learn to:

• • Discuss the benefits of different types of machine learning for solving business problems

• • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems

• • Explain how data scientists use AWS tools and ML to solve a common business problem

• • Summarize the steps a data scientist takes to prepare data

• • Summarize the steps a data scientist takes to train ML models

• • Summarize the steps a data scientist takes to evaluate and tune ML models

• • Summarize the steps to deploy a model to an endpoint and generate predictions

• • Describe the challenges for operationalizing ML models

• • Match AWS tools with their ML function

Who will the Course Benefit?

- Development Operations (DevOps) engineers

• - Application developers

Requirements

We recommend that attendees of this course have:

• • AWS Technical Essentials

• • Entry-level knowledge of Python programming

• • Entry-level knowledge of statistics

NOTE: Course technical content is subject to change without notice.

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Course Contents

Module 1: Introduction to Machine Learning



• Benefits of machine learning (ML)

• Types of ML approaches

• Framing the business problem

• Prediction quality

• Processes, roles, and responsibilities for ML projects



Module 2: Preparing a Dataset



• Data analysis and preparation

• Data preparation tools

• Demonstration: Review Amazon SageMaker Studio and Notebooks

• Hands-On Lab: Data Preparation with SageMaker Data Wrangler



Module 3: Training a Model



• Steps to train a model

• Choose an algorithm

• Train the model in Amazon SageMaker

• Hands-On Lab: Training a Model with Amazon SageMaker

• Amazon CodeWhisperer

• Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks



Module 4: Evaluating and Tuning a Model



• Model evaluation

• Model tuning and hyperparameter optimization

• Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker



Module 5: Deploying a Model



• Model deployment

• Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction



Module 6: Operational Challenges



• Responsible ML

• ML team and MLOps

• Automation

• Monitoring

• Updating models (model testing and deployment)



Module 7: Other Model-Building Tools



• Different tools for different skills and business needs

• No-code ML with Amazon SageMaker Canvas

• Demonstration: Overview of Amazon SageMaker Canvas

• Amazon SageMaker Studio Lab

• Demonstration: Overview of SageMaker Studio Lab

• (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint