What you'll learn
- 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
Who should take this course
- Solutions architects
- Data engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
What experience you'll need
- 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
Type: Classroom (virtual and in person)
Length: 4 Days
Need more information?
Download the course outline for more information about what this course covers.
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