COURSE LIST / COURSE BY TECHNOLOGY VENDORS / AWS

Practical Data Science with Amazon SageMaker

Course Summary:

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 Duration: 1 Day

Course Content:

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