Learn by Hands on Labs with AJ - AWS Sagemaker Canvas
Welcome to February Meetup - This session is for everyone who just start or wish to start their career in Machine Learning.
Agenda:
Machine Learning Use Case - Student Dropout Prediction
Uploaded a CSV file containing student data to an S3 bucket
Created an AWS Glue DataBrew recipe to clean, transform and enrich the dataset
Used Amazon SageMaker to build and train a binary classifier model to predict student dropout
Registered and deployed the trained model as a SageMaker endpoint
AWS AI Services from Canvas
Used Amazon Textract to extract text and data from scanned documents
Demonstrated how Textract can be used for document analysis like OCR, PII detection etc.
Used Amazon Comprehend to perform sentiment analysis on text snippets
AWS Bedrock from Canvas
Blog Post Comparison
Created a blog post summarizing the outputs and predictions of different ML models
Compared the performance of models trained on the raw vs. cleaned dataset
The hands-on labs provided a good introduction to building an end-to-end ML workflow on AWS using services like S3, Canvas, SageMaker, Textract and Comprehend. Participants found value in seeing how these services can be combined to solve real-world problems. The labs also helped demonstrate the power of AWS for AI/ML applications.
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