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This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.

  • Course Start Date: 2022-01-10
  • Time: 08:30:00 - 16:30:00
  • Duration: 4 days 08:30 AM - 04:30 PM
  • Location: Virtual
  • Delivery Method(s): Virtual Instructor Led
$3,650.00
REGULAR PRICE $3,650.00 Save $0.00
January 10th
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Course Outline

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

Lessons

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.

WHAT YOU'LL LEARN

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

OUTLINE

Classroom Live Outline

Day One

  • 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

Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • 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

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 optimization

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 optimization

Day Four

Lab 4: Feature Engineering (including project work)

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

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

Cancellation Policy

We require 16 calendar days notice to reschedule or cancel any registration. Failure to provide the required notification will result in 100% charge of the course. If a student does not attend a scheduled course without prior notification it will result in full forfeiture of the funds and no reschedule will be allowed. Within the required notification period, only student substitutions will be permitted. Reschedules are permitted at anytime with 16 or more calendar days notice. Enrollments must be rescheduled within six months of the cancel date or funds on account will be forfeited.

Training Location

Online Classroom
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About Global Knowledge

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Global Knowledge is the world's leading learning services and professional development solutions provider. We deliver learning solutions to support customers as they adapt to key business transformations and technological advancements that drive the way that organizations around the world differentiate themselves and thrive. Our learning programs, whether designed for a global organization or an individual professional, help businesses close skills gaps and foster an environment of continuous talent development.

Training Provider Rating

This vendor has an overall average rating of 4.38 out of 5 based on 431 reviews.

I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from ... Read more
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I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from the capturing of what was presented as it was 5-6 hours of watching a screen before getting to the actual labs. There has to be a better way to lay out this particular course. In my previous course, the lectures were broken up by labs which worked out fantastic and kept you engaged in the course. There were days when in order to actually complete the labs, would go over the 9PM day end time frame. Was able to get the primary labs done, but if you want to get all the content completed, you cannot complete it in the window of this course, you will need to come back on your own time.

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Instructor was great
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Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platfo ... Read more
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Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platform.. from adobe to something called zoom; I had to call support line cause it stated our instructor wasn’t present. Thankfully I called cause everyone online was in the adobe virtual classroom waiting for what looked like a teacher who didn’t show up for class (IT didn’t get anything resolved until 10mins after start time). I felt like he was really getting hung up on very basic knowledge for the first half of the course (talking about how to create tabs and drag formulas as an example). I completed files a few times before he was done explaining. There was a scheduled fire drill for them (roughly 30mins)that also cut into our time, which wasn’t deducted from the hour lunch break or the two, fifteen min breaks. I also really wish he touched base more on the automating workbook functions portion which we barely did. I'm happy there were/are those study guides (learning videos) and exams to take on my own time that I hope after I've had the class are still available for me to learn from.

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