Length
4 days

Overview

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction.

You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

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

Please note: students are required to bring their own laptop/tablet and power cable for this course.

Key Topics

Detailed Info
  • Introduction to Machine Learning and the ML Pipeline
  • Preprocessing
  • Model Evaluation
  • Deployment
  • Introduction to Amazon SageMaker
  • Problem Formulation
  • Model Training
  • Feature Engineering and Model Tuning
Skills Gained
Key Topics
Target Audience
Prerequisites

Skills Gained

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

Key Topics

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
  • 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

Checkpoint 1 and Answer Review

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

Checkpoint 2 and Answer Review

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

Checkpoint 3 and Answer Review

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

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

Target Audience

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

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected]

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
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The supply of this course by DDLS 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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Virtual Classroom
September 9 2021 - September 14 2021

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