AWS-DATASCI | Practical Data Science with Amazon SageMaker
 
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.
 
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
 
Prerequisites
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
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
 
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.