AWS Certified Generative AI Developer – Professional Exam Prep and Study Guide | Course Playlist - Feature Engineering on AWS: TF-IDF Extraction Using SageMaker Processing

GitHub: https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/16-sagemaker-processing-text-feature-extraction-tfidf-ngram.md Transform raw text into actionable machine learning features using Amazon SageMaker Processing Jobs. In this hands-on tutorial, you’ll learn how to upload data to S3, create a Python processing script, run a SageMaker job using the built-in Scikit-learn container, and generate TF-IDF features for customer review data. This walkthrough covers everything from environment setup to reviewing the generated output files in Amazon S3 — including fixing common issues during execution. 📘 What You’ll Learn Uploading datasets to Amazon S3 programmatically Creating a custom Python script for text preprocessing Using NLTK tokenization inside SageMaker containers Applying TF-IDF vectorization with unigrams and bigrams Running a SageMaker Processing Job using the Scikit-learn container Saving processed output files back to S3 Reviewing IAM role permissions used by the processing job Inspecting S3 input and output folders Debugging common file-name and path issues 🧠 What is TF-IDF? TF-IDF (Term Frequency–Inverse Document Frequency) is a powerful text-feature extraction technique used in: Search engines Information retrieval Text classification Sentiment analysis NLP pipelines The tutorial gives you a complete end-to-end workflow to generate these features using SageMaker. ⚠️ Cost Warning Amazon SageMaker Processing Jobs and S3 storage are not free. Even short jobs can incur charges. If you want to avoid costs, feel free to watch the tutorial without running it.