Optimizing Cloud Solutions for Student Success

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This project explores cloud computing applications in machine learning through the Student Success Advisor case study. It discusses optimizing cloud solutions, deploying and scaling the advisor using AWS architecture, and evaluating costs associated with S3, SageMaker, and Lambda. Additionally, it addresses privacy measures like IAM and KMS, summarizes key lessons on cost and scaling, and provides references related to AWS, education, and student performance.