How to Deploy a Serverless Spam Classifier Using Scikit-Learn, AWS Lambda, & API Gateway
The article outlines a step-by-step guide to building and deploying a serverless spam classifier using Scikit-learn, AWS Lambda, and API Gateway. It emphasizes the transition from machine learning experimentation in notebooks to scalable, real-time production systems. The solution leverages cloud services for cost-efficiency, modularity, and independent model updates.
- ▪The spam classifier uses Scikit-learn for model development and AWS Lambda, S3, and API Gateway for deployment.
- ▪Text data is converted into numerical features using TF-IDF vectorization to enable machine learning processing.
- ▪The model is trained with Logistic Regression and evaluated for accuracy on unseen test data.
- ▪The system allows real-time message classification and supports independent retraining and updates.
- ▪Prerequisites include Python proficiency, an AWS account, and access to libraries like scikit-learn and joblib.
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April 30, 2026 / #Machine Learning How to Deploy a Serverless Spam Classifier Using Scikit-Learn, AWS Lambda, & API Gateway Rakshath Naik In today's digital world, spam is no longer just an annoyance - it's a growing security threat. To combat this, developers often turn to machine learning to build intelligent filters that can distinguish legitimate emails from malicious ones. While building a machine learning model in a notebook is relatively straightforward, the real challenge lies in the last mile: deploying that model into a scalable, production-ready system that users can actually interact with. In this project, I built an end-to-end serverless spam classifier, combining Scikit-learn for model development with AWS Lambda, Amazon S3, and Amazon API Gateway for deployment.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More .
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