IBFIT11: THE MACHINE LEARNING PIPELINE ON AWS-P200623GUW
THE MACHINE LEARNING PIPELINE ON AWS-P200623GUW
Course Duration
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
Course Overview
NTUC LearningHub Course Code: IBFIT11
This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. You 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, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Next available schedule
Course Objectives
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
Pre-requisites
- Knowledge, Skills & Experience
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- Participants are required to attend AWS Cloud Practitioner Essentials (IBFIT36) Course 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
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- Hardware & Software
This course will be conducted as a Virtual Live Class (VLC) via Zoom platform. Participants must have a laptop or a desktop with “Zoom Client for Meetings” installed. This can be downloaded from https://zoom.us/download
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Minimum Specs |
Recommended Specs |
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Processor / CPU |
1.6 GHz or faster, 2-core Intel Core i3 or equivalent |
1.8 GHz, 2-core Intel Core i3 or equivalent |
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Memory |
4 GB RAM |
8 GB RAM |
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Hard Disk |
10 GB available storage space |
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Display |
1280 x 768 screen resolution |
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Graphics |
2GB Graphics Card |
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Others |
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Software |
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Course Outline
Day One
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
- 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
- Recap and Checkpoint #1
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
- 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 optimisation
Day Four
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
- Post-Assessment
- Wrap-up
Additional Details
Medium of Instruction & Trainer
Medium of Instruction: English
Price
| Course commencing after 1 July 2021 | |||
| (90% funded) | |||
| Full Course Fee | $3,400.00 | ||
| Full Course Fee with GST | $3,638.00 | ||
| Nett amount after Funding | |||
| Singapore Citizens & Permanent Residents** | Individual | $363.80* | |
| Corporate | $578.00 |
* includes 7% GST
Eligibility
Individual:
- Singapore Citizens or Singapore Permanent Residents physically based in Singapore
- Trainee has to complete 100% attendance and pass all relevant assessments and examinations
- NTUC LearningHub reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria
Corporate:
- Singapore Citizens or Singapore Permanent Residents physically based in Singapore
- Company must be Financial Institutions regulated by MAS (licensed or exempted from licensing). FinTech firms must be certified by Singapore FinTech Association (SFA)
- Trainee has to complete 100% attendance and pass all relevant assessments and examinations
- Eligible company-sponsored trainees will be able to clock CPD hours upon successful course completion
Class Allowance
For all courses commencing after 1st July 2021 and before 31st December 2021 :
- Singapore Citizens and permanent residents of all ages will receive course fee subsidies of 90%
- The course fee for individuals and corporates are shown in the pricing table.
- Training Allowance – $10 per training and assessment hour (Sponsoring companies in Finance and Banking)
Terms and 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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