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AWS Authorized Training Course - 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
PrerequisitesML 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.:

Prerequisites

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Entry-level knowledge of Python programming
  • Entry-level knowledge of statistics

THIS COURSE IS NOT ELIGIBLE FOR TRAINING BUNDLES.

Purpose
Learn to collaborate efficiently with Data Scientists and build applications that integrate with ML
Audience
Developers looking to build applications that integrate with ML
Role
Development Operations (DevOps) Engineers | Application Developers
Skill Level
Intermediate
Style
Presentations | Hands-on Labs | Demonstrations
Duration
1 Day

 

Course Objectives
  • 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

What You'll Learn:

In the AWS Authorized Training Course - Practical Data Science with Amazon SageMaker training course, you'll learn:
  • Module 1: Introduction to Machine Learning
    • Benefits of machine learning (ML)
    • •ypes 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

AWS Authorized Training Course - Practical Data Science with Amazon SageMaker

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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