Cisco
Business Transformation
ASA
Practical Data Science with Amazon SageMaker (PDSASM)

As artificial intelligence and machine learning (AI/ML) are quickly becoming part of our day-to-day, it is becoming increasingly more important to understand how to collaborate efficiently with data scientists and build applications that integrate with ML. The Practical Science with Amazon SageMaker course will help you in your developer or DevOps engineer role understand the basics of ML and the steps involved in building ML models using Amazon SageMaker Studio. In this one-day, classroom training course an expert AWS instructor will walk you through how to prepare data and train, evaluate, tune, and deploy ML models.

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Duration: 1 Day
About the course

Prerequisites:

We recommend that attendees of this course have the following skills and knowledge:

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

Course Objectives:

In this course, the student 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
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
Who Should Attend

This course is intended for:

  • Development Operations (DevOps) engineers
  • Application developers