AMA30 – Practical Data Science with Amazon SageMaker


AMA30 - Practical Data Science with Amazon SageMaker

Course description

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.


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

Course objectives

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

Intended audience

This course is intended for:
Development Operations (DevOps) engineers
Application developers


We recommend that attendees of this course have:
AWS Technical Essentials
Entry-level knowledge of Python programming
Entry-level knowledge of statistics

Course outline

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
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

Записаться на курс

Длительность: 1 день (8 часов)
Код курса: АМА30

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