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A complete machine learning operations project that provides credibility scores for news articles through automated pipelines, experiment tracking, monitoring, and cloud deployment.
Section 01
A complete machine learning operations project that provides credibility scores for news articles through automated pipelines, experiment tracking, monitoring, and cloud deployment.
Section 02
Section 03
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.
Section 04
The project adopts the FTIM (Feature–Training–Inference–Monitoring) architecture, realizing a complete closed loop from raw data to production-level services:
Section 05
The system integrates multiple data sources:
Section 06
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.
Section 07
Section 08
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