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The Machine Learning Pipeline on AWS is a Program

The Machine Learning Pipeline on AWS

Self-paced

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Full program description

The Machine Learning Pipeline on AWS

 

Programme Code:P200623GUW

Course Overview

NTUC LearningHub Course Code: IBFIT11

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. You 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, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

Who Should Attend

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

 

Course Duration

4 Days / 30 Hours

Course Outline

Day One

Module 0: Introduction

  • Pre-assessment

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

Module 2: Introduction to Amazon SageMaker

  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Lab 1: Introduction to Amazon SageMaker

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
  • Problem Formulation Exercise and Review
  • Project work for Problem Formulation

Day Two

  • Recap and Checkpoint #1

Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualisation
  • Lab 2: Data Preprocessing (including project work)

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

Day Three

  • Recap and Checkpoint #2

Module 6: Model Training

  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Lab 3: Model Training and Evaluation (including project work)
  • Project Share-Out 1

Module 7: Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimisation

Day Four

Lab 4: Feature Engineering (including project work)

  • Recap and Checkpoint #3

Module 8: Module Deployment

  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge

Module 9: Course Wrap-Up

  • Project Share-Out 2
  • Post-Assessment
  • Wrap-up

 

Course Objectives

In this course, you will learn how 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 in 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

 

Pre-requisites

We recommend that attendees of this course have the following prerequisites:

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic understanding of working in a Jupyter notebook environment

 

Medium of Instruction & Trainer

Medium of Instruction: English

 

Price

Course Fee

 

Full Course Fee

$ 3,400.00

 

Full Course Fee with GST

$  3,638.00

 
     

Nett amount after Funding

Singapore Citizens & Permanent Resident**

Individual

 $  181.90*

 Corporate

 $  408.00*

Others

$  3,638.00 *

 

Terms and conditions apply. NTUC LearningHub reserve the right to make changes or improvements to any of the products described in this document without prior notice.

Prices are subject to other LHUB miscellaneous fees.

IBFIT11