About Course
Learn how to use the machine learning ML pipeline with Amazon SageMaker with hands on exercises and four days of instruction You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train evaluate tune and deploy ML models Hands on learning is a key component of this course so you ll choose a project to work on and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline You ll have a choice of projects fraud detection recommendation engines or flight delays
Prerequisite Courses
- Basic knowledge of Python
- Basic understanding of working in a Jupyter notebook environment
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Take this skills assessment to see if you have the required knowledge to skip AWS Technical Essentials, the first course in the AWS technical training path: https://www.exitcertified.com/training-resources/skills-assessments/aws-skills-assessment
Skills Gained
- 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
Who Can Benefit
- 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
Course Details
Day 1
- Module 0: Introduction
- Module 1: Introduction to Machine Learning and the ML Pipeline
- Module 2: Introduction to Amazon SageMaker
- Module 3: Problem Formulation
Day 2
- Module 3: Problem Formulation (continued)
- Module 4: Preprocessing
Day 3
- Module 5: Model Training
- Module 6: Model Evaluation