This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will learn to:

Select and justify the appropriate ML approach for a given business problem
Use the ML pipeline to solve a specific business problem
Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
Apply machine learning to a real-life business problem after the course is complete

1
  • Module 0: Introduction

  • Pre-assessment

2
  • Module 1: Introduction to Machine Learning and the ML Pipeline

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
    Overview of the ML pipeline
    Introduction to course projects and approach

3
  • Module 2: Introduction to Amazon SageMaker

  • Introduction to Amazon SageMaker
    Demo: Amazon SageMaker and Jupyter notebooks
    Hands-on: Amazon SageMaker and Jupyter notebooks

4
  • Module 3: Problem Formulation

  • Overview of problem formulation and deciding if ML is the right solution
    Converting a business problem into an ML problem
    Demo: Amazon SageMaker Ground Truth
    Hands-on: Amazon SageMaker Ground Truth
    Practice problem formulation
    Formulate problems for projects

5
  • Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
    Practice preprocessing
    Preprocess project data
    Class discussion about projects

5
  • Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
    Practice preprocessing
    Preprocess project data
    Class discussion about projects

6
  • Module 5: Model Training

  • Choosing the right algorithm
    Formatting and splitting your data for training
    Loss functions and gradient descent for improving your model
    Demo: Create a training job in Amazon SageMaker

7
  • Module 6: Model Evaluation

  • How to evaluate classification models
    How to evaluate regression models
    Practice model training and evaluation
    Train and evaluate project models
    Initial project presentations

8
  • Module 7: Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation
    Hyperparameter tuning
    Demo: SageMaker hyperparameter optimization
    Practice feature engineering and model tuning
    Apply feature engineering and model tuning to projects
    Final project presentations

9
  • Module 8: Deployment

  • How to deploy, inference, and monitor your model on Amazon SageMaker
    Deploying ML at the edge
    Demo: Creating an Amazon SageMaker endpoint
    Post-assessment
    Course wrap-up

Audience

This course is intended for Developers, Solutions Architects, Data Engineers, anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker.

Language

English

Prerequisites

We recommend that attendees of this course have: Basic knowledge of Python programming language Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch) Basic experience working in a Jupyter notebook environment

$2,700

Length: 4.0 days (32 hours)

Level:

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