COURSE OBJECTIVE:
In this course, you will learn 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 using 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
TARGET AUDIENCE:
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 PREREQUISITES:
We recommend that attendees of this course have:
• Basic knowledge of Python programming language
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Basic experience working in a Jupyter notebook environment
COURSE CONTENT:
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
FOLLOW ON COURSES:
Not available. Please contact.
