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The Machine Learning Pipeline on AWS

  • Length 4 days
  • Price  $3820 inc GST
Course overview
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Why study this course

This four-day course explores how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process 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.

This intermediate-level course is delivered through a mix of instructor-led training (ILT), hands-on labs, demonstrations, and group exercises.

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What you’ll learn

This course is designed to teach participants 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-optimised, and secure ML pipelines in AWS

  • Apply machine learning to a real-life business problem after the course is complete


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AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.


Who is the course for?

This course is intended for:

  • Developers

  • Solutions architects

  • Data engineers

  • Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning


Course subjects

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

  • Hands-on: Amazon SageMaker and Jupyter notebooks

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

Module 4: Preprocessing

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

  • Practice preprocessing

  • Preprocess project data and discuss project progress

  • Class discussion about projects

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 Evaluation

  • How to evaluate classification models

  • How to evaluate regression models

  • Practice model training and evaluation

  • Train and evaluate project models, then present findings

  • Initial project presentations

Module 7: Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation

  • Hyperparameter tuning

  • Demo: SageMaker hyperparameter optimisation

  • Practice feature engineering and model tuning

  • Apply feature engineering and model tuning to projects

  • Final project presentations

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

Please note: This is an emerging technology course. Course outline is subject to change as needed.


Prerequisites

It is recommended that attendees have the following prerequisites:

  • Basic knowledge of Python

  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

  • Basic understanding of working in a Jupyter notebook environment


Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.


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