E-MLP: THE MACHINE LEARNING PIPELINE ON AWS
THE MACHINE LEARNING PIPELINE ON AWS
Course Duration
Mode of Assessment
NA
Who Should Attend
- Participants are required to attend AWS Cloud Practitioner Essentials prior to attending this course.
- 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
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. They will 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.
Course Schedule
Next available schedule
Course Objectives
Upon completing this course, participants will be able 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
Pre-requisites
Upon completing this course, participants will be able 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
Course Outline
Day One
Module 0: Introduction
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 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
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
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
- Wrap-up
Certificate Obtained and Conferred by
- Certificate of Completion from NTUC LearningHub & AWS
Upon meeting at least 75% attendance, participants will receive a Certificate of Completion from NTUC LearningHub and AWS.
- External Certification Exam
This course will prepare participants to sit for AWS Certified Machine Learning – Specialty Exam.
Exam overview for AWS Certified Machine Learning – Specialty
Level: Specialty
Length: 180 minutes to complete the exam
Cost: 300 USD
Visit Exam Pricing for additional cost information.
Format: 65 questions; either multiple choice or multiple response.
Delivery method: Pearson VUE testing center or online proctored exam.
Exam voucher can be purchased here
** LHub will issue 1 x AWS Exam Voucher (worth USD300) for AWS Certified Machine Learning – Specialty Exam. Participants must attempt the exam before the exam voucher expiry date. Please note that LHub will not be issuing any exam voucher replacement.
Additional Details
Medium of Instruction: English
Trainer: Trainee Ratio is 1:20
Mode of Delivery: Online Instructor-Led Training via VLC
Courseware: AWS
Lab: Qwiklabs.
Price
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Course Fee and Government Subsidies |
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Individual Sponsored |
Company Sponsored |
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Non-SME |
SME |
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Before GST |
With GST |
Before GST |
With GST |
Before GST |
With GST |
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For International Students (Full Course Fee) |
$3,400.00 |
$3,672.00 |
$3,400.00 |
$3,672.00 |
$3,400.00 |
$3,672.00 |
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For Singapore Citizens and PRs aged 21 years and above |
$1,020.00 |
$1,292.00 |
$1,020.00 |
$1,292.00 |
$1,020.00 |
$1,292.00 |
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Funding Eligibility Criteria:
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Individual Sponsored Trainee |
Company Sponsored Trainee |
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Remarks:
Terms & 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.
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