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
your office
your city,
your province
your country
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.