WSQ-AWS The Machine Learning Pipeline on AWS
About This Course
This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process 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. Learners with little to no machine-learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
What You'll Learn
LO2: Integrate data into AWS for machine learning and support API-level integration.
LO3: Execute AWS machine learning application tests and verify functionality.
LO4: Highlight and rectify AWS machine learning application integration issues.
Entry Requirements
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.
- Minimum post secondary education.
- Trainocate will assess the learner's suitability if they are unable to meet the minimum entry requirements.