Duration
4 days.
Prerequisites
- 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
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 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
Who Can Benefit?
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
Outline for The Machine Learning Pipeline on AWS Training
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 4: Preprocessing
Day 3
- Module 5: Model Training
- Module 6: Model Evaluation
Day 4
- Module 7: Feature Engineering and Model Tuning
- Module 8: Deployment
12/18/2023 - 12/21/2023
09:00 AM - 05:00 PM
Eastern Standard Time
USD $2,970.00
01/09/2024 - 01/12/2024
09:00 AM - 05:00 PM
Eastern Standard Time
USD $2,970.00
01/30/2024 - 02/02/2024
12:00 PM - 08:00 PM
Eastern Standard Time
USD $2,970.00