The Machine Learning Pipeline on AWS

Course Modality

Instructor-led (classroom)

Course Level

Beginner

Course Time

90 MINUTES

Course Language

English

Course Overview

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.

Prerequisites

Basic knowledge of Python:

  • How to create functions, list comprehensions, dictionaries, AWS Lambda functions
  • How to import libraries using pip

 

Basic understanding of the AWS Cloud:

  • AWS Cloud infrastructure
  • How to navigate Amazon S3 and Amazon CloudWatch

Baseline understanding of how to write code cells and Markdown cells in a Jupyter notebook environment

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning approach that you can adapt to your learning style

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

  • Select and justify the appropriate ML approach to a problem
  • Build, train, evaluate, deploy, and fine-tune an ML model.
  • Apply the steps of the ML pipeline to solve the problem.
  • Describe best practices to create and manage an ML pipeline on AWS.
  • Identify the steps to apply ML to real business problems.

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Course Curriculum

Introduction

Exercise: Pre-assessment

Intro to ML and the ML Pipeline

Introduction to Amazon SageMaker

Lab 1: Introduction to Amazon SageMaker

Exercise: Choose your project

Problem formulation

Exercise: Formulate your project’s business problem

Checkpoint #1

Data preprocessing

Lab 2: Data preprocessing

Model training

Checkpoint #2

Model evaluation

Lab 3: Model Training and Evaluation

Exercise: Project presentations

Feature Engineering and Model Tuning

Checkpoint #3

Lab 4: Feature Engineering

Model deployment

Exercise: Final project share out

Exercise: Post-assessment

Course Wrap-up

Recommended Exams

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AWS Certified Machine Learning – Specialty

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

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