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AW233: Practical Data Science with Amazon SageMaker

Training: AWS™ - Cloud - Artificial Intelligence - Certifications

Participants receive a practical introduction to the development process of machine learning solutions with Amazon SageMaker. The course covers the steps for creating, training, and deploying an ML model. It addresses the daily work of a data scientist, complemented by instructor-led demonstrations and practical exercises.

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Possible course languages: German

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Agenda:

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

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

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

Evaluating and Tuning a Model
- Model evaluation
- Model tuning and hyperparameter optimization
- Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker

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

Operational Challenges
- Responsible ML
- ML team and MLOps
- Automation
- Monitoring
- Updating models (model testing and deployment)

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

Objectives:

In this course AW233 Practical Data Science with Amazon SageMaker, 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

Target audience:

This course AW233 Practical Data Science with Amazon SageMaker is intended for:

Development Operations (DevOps) engineers
Application developers

Prerequisites:

To participate in the course AW233 Practical Data Science with Amazon SageMaker at qSkills™, you should meet the following prerequisites::

Have completed the course AW110 AWS™ Technical Essentials
Possess basic knowledge in Python programming
Have basic knowledge in statistics

Description:

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course AW233 Practical Data Science with Amazon SageMaker, 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 AW233 Practical Data Science with Amazon SageMaker includes presentations, hands-on labs, and demonstrations.