# Real-Time News Credibility Scoring System: End-to-End MLOps Practice

> A complete machine learning operations project that provides credibility scores for news articles through automated pipelines, experiment tracking, monitoring, and cloud deployment.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-01T18:15:07.000Z
- 最近活动: 2026-06-01T18:18:18.710Z
- 热度: 163.9
- 关键词: MLOps, 机器学习, 虚假新闻检测, FastAPI, Streamlit, Airflow, MLflow, Google Cloud, 自然语言处理, 可信度评分
- 页面链接: https://www.zingnex.cn/en/forum/thread/mlops-eeb8e25c
- Canonical: https://www.zingnex.cn/forum/thread/mlops-eeb8e25c
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Real-Time News Credibility Scoring System: End-to-End MLOps Practice

A complete machine learning operations project that provides credibility scores for news articles through automated pipelines, experiment tracking, monitoring, and cloud deployment.

## Original Author and Source

- **Original Author/Maintainer:** Nishant Singh (realking46)
- **Source Platform:** GitHub
- **Original Project Title:** Real-Time-News-Credibility-Scoring-System
- **Project Link:** https://github.com/realking46/Real-Time-News-Credibility-Scoring-System
- **Release Date:** June 2026
- **Relevant Background:** IIT Roorkee and HSLU MLOps Course Project (Spring 2026)

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## Background: Trust Crisis in the Information Age

In today's digital age, the spread of fake news and misleading information far outpaces the verification capabilities of traditional media. Users often struggle to judge the credibility of content when browsing news, which not only affects personal decisions but may also pose a threat to social stability. How to use technical means to automatically evaluate the credibility of news articles has become an important application scenario in the field of machine learning.

The project introduced in this article is a complete solution built to address this problem. It is not just a simple classification model, but an end-to-end MLOps system covering data ingestion, feature engineering, model training, inference services, monitoring and alerting, and cloud deployment.

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## System Architecture Overview

The project adopts the FTIM (Feature–Training–Inference–Monitoring) architecture, realizing a complete closed loop from raw data to production-level services:

## Data Layer

The system integrates multiple data sources:
- **Static Datasets:** LIAR Dataset (fact-checking of political statements), FakeNewsNet (real and fake news from PolitiFact and GossipCop sources)
- **Real-Time Data Streams:** RSS feeds, NewsAPI (optional), BeautifulSoup web scraping

## Feature Engineering Layer

Raw text is converted into feature vectors usable by the model through techniques like TF-IDF. It also supports feature storage and version management to ensure consistency between training and inference phases.

## Model Training Layer

- Baseline Model: Traditional machine learning model based on Scikit-learn
- Deep Learning Model: DistilBERT text classification implemented with PyTorch
- Experiment Tracking: MLflow records hyperparameters, metrics (accuracy, precision, recall, F1 score), and model artifacts

## Inference Service Layer

- **FastAPI:** Provides high-performance RESTful API prediction endpoints
- **Streamlit:** Builds a user-friendly visualization interface
- **Response Format:** JSON output includes prediction label, confidence, credibility score (0-100), and risk level (low/medium/high)