The Machine Learning Pipeline on AWS

Part 1: Introduction to Machine Learning and the ML Pipeline

1. Overview of machine learning, including use cases, types of machine learning, and key concepts
2. Overview of the ML pipeline
3. Introduction to course projects and approach

Part 2: Introduction to Amazon SageMaker

1. Introduction to Amazon SageMaker
2. Demo: Amazon SageMaker and Jupyter notebooks
3. Lab 1: Introduction to Amazon SageMaker

Part 3: Problem Formulation

1. Overview of problem formulation and deciding if ML is the right solution
2. Converting a business problem into an ML problem
3. Demo: Amazon SageMaker Ground Truth
4. Hands-on: Amazon SageMaker Ground Truth
5. Problem Formulation Exercise and Review
6. Project work for Problem Formulation

Part 4: Preprocessing

1. Overview of data collection and integration, and techniques for data preprocessing and visualization
2. Lab 2: Data Preprocessing (including project work)

Part 5: Model Training

1. Choosing the right algorithm
2. Formatting and splitting your data for training
3. Loss functions and gradient descent for improving your model
4. Demo: Create a training job in Amazon SageMaker

Part 6: Model Training

1. How to evaluate classification models
2. How to evaluate regression models
3. Practice model training and evaluation
4. Train and evaluate project models
5. Lab 3: Model Training and Evaluation (including project work)
6. Project Share-Out 1

Part 7: Feature Engineering and Model Tuning

1. Feature extraction, selection, creation, and transformation
2. Hyperparameter tuning
3. Demo: SageMaker hyperparameter optimization
4. Lab 4: Feature Engineering (including project work)

Part 8: Module Deployment

1. How to deploy, inference, and monitor your model on Amazon SageMaker
2. Deploying ML at the edge

Part 9: Course Wrap-Up

1. Project Share-Out 2
2. Post-Assessment
3. Wrap-up

• 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
• Basic knowledge of Python
• Basic understanding of working in a Jupyter notebook environment
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

• 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

A full refund will be issued for class cancellations made at least 15 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 15 business days from the course start date and for No‑Shows (students who do not attend class).
For reschedules made within 15 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of 90 days from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.