Applied ML Systems: From Data to Demo

中文导读

真实的 ML 项目往往不是“训练一个模型”这么简单。更重要的是：数据怎么来，特征或文本怎么处理，模型怎么评估，结果怎么解释，最后怎么让别人可以使用。

Working outline

1. Problem framing
2. Data collection and cleaning
3. Baselines and model choices
4. Evaluation and error analysis
5. Reporting results
6. Demo or deployment

Reference materials to digest

• Google Machine Learning Guides
• scikit-learn model selection and evaluation
• Hugging Face Course

My angle

This category keeps the blog useful for broader AI engineer, ML engineer, data scientist, and research engineer roles.