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AI-Powered Content Summarization

This project leverages Natural Language Processing (NLP) techniques to automatically summarize extensive content. By effectively condensing information, it significantly enhances readability and comprehension, ultimately saving users' time and improving information accessibility.

14 lectures

What You Will Learn

Master the end-to-end Machine Learning project lifecycle for Natural Language Processing (NLP), from data ingestion to cloud deployment.
Implement modular and scalable ML pipelines using object-oriented programming (OOP) principles.
Gain experience building RESTful web applications with FastAPI for serving ML model predictions
Develop and deploy Dockerized NLP applications on AWS cloud infrastructure.

System Architecture

AI-Powered Content Summarization Architecture Diagram

High-level architecture overview of the AI-Powered Content Summarization .

What You'll Build

  • An "AI-Powered Content Summarization System" capable of generating concise abstractive summaries from long-form text or dialogue.
  • A fine-tuned Google Pegasus model trained on the SAMSum dataset with optimized inference parameters.
  • A robust 5-stage machine learning pipeline consisting of Data Ingestion, Validation, Transformation, Model Training, and Evaluation.
  • A FastAPI-based backend API that accepts input text, triggers model predictions, and returns high-quality summaries.
  • A fully automated CI/CD pipeline that builds Docker images and deploys the summarization application on AWS cloud servers.

Project Instructor

Boktiar Ahmed Bappy

Boktiar Ahmed Bappy

5+ years exp
LinkedIn
AI-Powered Content Summarization
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