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Illustration: AWS Machine Learning with Amazon SageMaker AI course

AWS Machine Learning with Amazon SageMaker AI

The AWS Machine Learning with Amazon SageMaker AI course introduces participants to the main stages involved in building a Machine Learning solution on AWS, from data preparation to model deployment.

Over two days, participants will learn how to use key AWS services for Machine Learning, including Amazon S3, Amazon SageMaker AI, IAM, and Amazon CloudWatch. The course focuses on hands-on learning through demonstrations and practical exercises covering data preparation, model training, model evaluation, deployment, and predictions.

The course also provides an introduction to MLOps, model monitoring, and Amazon Bedrock, helping participants understand how Machine Learning and generative AI fit into the AWS ecosystem.

2 days 9:00 a.m. to 4:30 p.m. Online, Montreal, Quebec City

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The AWS Machine Learning with Amazon SageMaker AI course at a glance

Length
2 days
Schedule
9:00 a.m. to 4:30 p.m.
Format
Live virtual class or in person in Montreal and Quebec City
Audience
Tous
Price
$1,141 per participant, or $969 for public bodies and non-profits (plus taxes)
Language
English

Doussou Formation and AWS Machine Learning with Amazon SageMaker AI: key facts

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AWS Machine Learning with Amazon SageMaker AI course outline

Introduction to Machine Learning

  • Understand Artificial Intelligence and Machine Learning
  • Difference between AI, Machine Learning, and Deep Learning
  • Understand models, training, and predictions
  • Understand features and labels
  • Supervised and unsupervised learning
  • Classification, regression, and clustering
  • Examples of Machine Learning use cases in organizations

Machine Learning on AWS

  • Overview of the AWS Machine Learning ecosystem
  • Introduction to Amazon SageMaker AI
  • Role of Amazon S3 in a Machine Learning project
  • Introduction to IAM roles and permissions
  • Introduction to Amazon CloudWatch for monitoring
  • Understand the architecture of a Machine Learning solution on AWS

Preparing the AWS Environment

  • Access the AWS Management Console
  • Select an AWS Region
  • Create and configure an Amazon S3 bucket
  • Upload a dataset to Amazon S3
  • Access Amazon SageMaker AI
  • Configure the required permissions
  • Explore the SageMaker working environment

Understanding and Preparing Data

  • Identify the data used for model training
  • Understand rows, columns, and variables
  • Identify numerical and categorical variables
  • Identify the target variable
  • Handle missing values
  • Identify and remove duplicates
  • Understand outliers
  • Select relevant features

Transforming Data for Machine Learning

  • Clean a dataset
  • Transform data types
  • Encode categorical variables
  • Normalize data
  • Introduction to Feature Engineering
  • Create a dataset ready for model training
  • Discover data preparation tools in SageMaker

Preparing Training and Test Data

  • Understand the purpose of training data
  • Understand the purpose of validation data
  • Understand the purpose of test data
  • Split a dataset into multiple subsets
  • Understand model generalization

Training a Model with Amazon SageMaker AI

  • Understand how Machine Learning algorithms work
  • Select an algorithm appropriate for the problem
  • Understand SageMaker Training Jobs
  • Configure a training job in SageMaker
  • Understand hyperparameters
  • Select computing resources
  • Launch a training job
  • Monitor the training process
  • Retrieve the trained model

Evaluating Model Performance

  • Understand why models must be evaluated
  • Measure Accuracy
  • Understand Precision
  • Understand Recall
  • Discover the F1 Score
  • Understand the confusion matrix
  • Compare predictions with expected results
  • Interpret model performance

Understanding Underfitting and Overfitting

  • Understand Underfitting
  • Understand Overfitting
  • Identify a model that does not generalize well
  • Understand how data affects model performance
  • Understand the impact of hyperparameters
  • Improve a model progressively

Optimizing a Machine Learning Model

  • Compare multiple models
  • Adjust hyperparameters
  • Improve the features used by the model
  • Introduction to Hyperparameter Tuning
  • Retrain a model
  • Compare performance results
  • Select the best-performing model

Deploying a Model with Amazon SageMaker AI

  • Understand the concept of inference
  • Create a deployable model
  • Understand SageMaker endpoints
  • Configure an endpoint
  • Deploy a model
  • Send new data to the model
  • Retrieve predictions
  • Discover real-time inference
  • Introduction to batch and serverless inference

Integrating a Model into an AWS Application

  • Understand how an application consumes a Machine Learning model
  • Call a SageMaker endpoint using Python
  • Introduction to AWS Lambda
  • Introduction to Amazon API Gateway
  • Understand an API Gateway, Lambda, and SageMaker architecture
  • Test predictions from an application

Introduction to MLOps on AWS

  • Understand the Machine Learning model lifecycle
  • Version Machine Learning models
  • Introduction to SageMaker Model Registry
  • Register different versions of a model
  • Understand model approval workflows
  • Manage model deployment to production
  • Understand the model retraining process

Monitoring a Model in Production

  • Introduction to Amazon CloudWatch
  • Review endpoint metrics
  • Review logs
  • Understand Data Drift
  • Understand Model Drift
  • Introduction to SageMaker Model Monitor
  • Identify model performance degradation
  • Determine when a model should be retrained

Security and Cost Management

  • Understand IAM roles used by SageMaker
  • Apply the principle of least privilege
  • Control access to data stored in Amazon S3
  • Understand data protection
  • Identify the main SageMaker cost drivers
  • Understand Training Job costs
  • Understand endpoint costs
  • Remove unused AWS resources after labs

Introduction to Generative AI on AWS

  • Understand the difference between traditional Machine Learning and generative AI
  • Understand Foundation Models
  • Understand Large Language Models
  • Introduction to Amazon Bedrock
  • Understand the concept of prompting
  • Introduction to embeddings
  • Introduction to Retrieval-Augmented Generation (RAG)
  • Identify use cases for SageMaker AI and Amazon Bedrock

Hands-on Workshop: End-to-End Machine Learning Project

  • Import a dataset into Amazon S3
  • Explore and prepare the data
  • Split the data into training and test datasets
  • Configure Amazon SageMaker AI
  • Train a Machine Learning model
  • Evaluate model performance
  • Improve the model
  • Deploy the model to a SageMaker endpoint
  • Send new data to the deployed model
  • Generate and interpret predictions
  • Review metrics and logs
  • Remove AWS resources used during the workshop

Prices and practical information

Preferred rate

$969 / participant

Public bodies and non-profits · plus taxes

Standard price

$1,141 / participant

Businesses and individuals · plus taxes

Practical information

  • Length: 2 days
  • Schedule: 9:00 a.m. to 4:30 p.m.
  • Format: Live virtual class or in person in Montreal and Quebec City
  • Payment: online (PayPal or card) when registering, or by invoice for organizations
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Participant reviews

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Why take this AWS Machine Learning with Amazon SageMaker AI course

Objectives: what you will be able to do

  • Understand the fundamental concepts of Machine Learning
  • Discover the main AWS services used for Machine Learning
  • Prepare and manage data with Amazon S3 and SageMaker AI
  • Train and evaluate a Machine Learning model
  • Deploy a model and generate predictions
  • Understand the basics of MLOps and model monitoring
  • Apply security and cost management best practices
  • Discover Amazon Bedrock and generative AI on AWS

Prerequisites

  • Have a general understanding of AWS cloud concepts
  • Be comfortable navigating the AWS Management Console
  • Have basic knowledge of Amazon S3 and IAM
  • Have basic Python programming skills
  • Be comfortable working with simple datasets, including CSV files
  • No previous Machine Learning experience is required

What is included

  • Course materials and exercise files for each participant
  • Certificate of attendance
  • Optional personalized coaching after the course
  • In public in-person sessions:
    • Tea and coffee
    • Lunch at a nearby restaurant
    • Parking (in some cities)
    • Wi-Fi access

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