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FROM SCHOLAR TO BUILDER: MY AWS AI/ML JOURNEY AS A FIRST-YEAR STUDENT
- As a first-year student, participating in a scholarship program introduced them to AI and ML workflows within the AWS ecosystem, changing their perspective on large scalable solutions.
- The course they took through Udacity sponsored by AWS focused on AI programming with Python and included creating two projects to apply knowledge to real-world applications.
- After completing the foundation course, they qualified for the Advanced Cohort: Machine Learning Fundamentals Nanodegree, where they used AWS services like Amazon SageMaker Studio and Amazon S3.
- Working on cloud technologies like AWS opened up a new world of scalable AI and ML implementations, providing hands-on experience with real-time applications and a variety of development tools.
- They embarked on projects like predicting bike sharing demand with AutoGluon using AWS SageMaker to gain insights and deep dive into SageMaker Studio.
- Setting up SageMaker Studio involved creating users in the domain to manage notebooks and experiments, with tools like JupyterLab environment hosted on AWS infrastructure.
- They worked on a capstone project for a logistics company using AWS cloud services, focusing on building an image classification model to optimize delivery efficiency.
- Utilizing Step Functions and Lambda functions, they automated the pipeline for image classification, achieving 94% accuracy with checks for prediction reliability.
- While exploring AutoML with Amazon SageMaker Canvas for a flower image classification model, they marveled at the no-code tool's intuitiveness for ML beginners.
- The exposure to AWS services like S3, SageMaker Studio, AutoML, Lambda, Step Functions, and IAM Roles provided real-world experience in building and managing cloud-based AI systems.
- Transitioning from a learner to a builder in their AI and ML journey, they emphasized the importance of curiosity, community support, and continuous experimentation.
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