Serverless Spam Classifier with Scikit‑learn on AWS Lambda
Deploy a Scikit‑learn spam classifier to AWS Lambda by packaging scikit‑learn in a Lambda layer, uploading model artifacts to S3, and exposing the function via API Gateway.
Build the Lambda layer with scikit‑learn, upload model artifacts to S3, and deploy the Lambda function behind API Gateway; test the endpoint with sample payloads.
Summary
The article describes building a serverless spam classifier that uses Scikit‑learn for model training and AWS Lambda, S3, and API Gateway for deployment. The model is trained on a labeled email dataset, vectorized with TF‑IDF, and classified with Logistic Regression, achieving ~94% accuracy on the test set. The trained model and vectorizer are serialized to .pkl files using Joblib and uploaded to an S3 bucket. An AWS Lambda layer is created to bundle scikit‑learn and joblib, circumventing the Lambda deployment package size limit. The Lambda function loads the model and vectorizer from S3, processes incoming JSON requests, and returns spam probability. API Gateway exposes the Lambda as a RESTful endpoint, enabling real‑time classification. The architecture decouples model updates from code deployments, allowing the model to be retrained and overwritten in S3 without redeploying the Lambda. The guide emphasizes modularity, cost efficiency, and scalability, making the classifier suitable for production use.
Key changes
- Train TF‑IDF vectorizer and Logistic Regression model, serialize to .pkl.
- Upload .pkl files to S3 bucket for model storage.
- Create Lambda layer with scikit‑learn and joblib, zip and upload to S3.
- Deploy Lambda function that loads model from S3 and processes requests.
- Expose Lambda via API Gateway REST endpoint.
- Decouple model updates from code deployments.
- Achieve ~94% accuracy on test set.
- Use inverse‑probability weighting? (not applicable).