翻译：
How is AI (artificial intelligence) data processed?
人工智能的数据处理是把原始数据转化为可用于建模的规范样本的过程，构成上包括数据清洗与预处理（特征选择、标准化、归一化）以及数据质量管理，解决的是无效、错误、缺失、重复与错配数据拉低模型精度的问题，并不解决模型上线后因部署环境与业务变化造成的效果衰减问题。
1. AI (Artificial Intelligence) data cleaning and processing
(1) Data cleaning: Data cleaning refers to the identification and elimination of invalid, inaccurate, incomplete, duplicate or redundant data. The cleaned data can improve model accuracy and reduce errors when building machine learning models.
(2) Data preprocessing: Data preprocessing refers to converting data into a format suitable for machine learning and preparing the data for subsequent analysis. It usually includes steps such as feature selection, standardization and normalization.
2. Importance of AI (Artificial Intelligence) data quality
Data quality is whether the index data is accurate, complete, consistent, reliable, and realistic. Data quality is very important for the accuracy and reliability of machine learning models, as any low-quality data will directly affect the results of the model.
Here are some data quality issues:
(1) Data errors and typos: Data errors and typos can confuse and lose meaning, and make machine learning algorithms produce wrong results.
(2) Missing data: In the data set, there may be some missing data, if not processed, it will lead to errors in the model.
(3) Duplicate data: Duplicate data may generate unnecessary noise in the data set and occupy storage space.
(4) Data mismatch: Data mismatch refers to the difference between different data quality or structure. Data misalignment can negatively impact machine learning models and reduce the accuracy of predictions.
In general, data cleaning and processing is a very important step in machine learning models. Data quality is important for the accuracy and reliability of machine learning models, so inaccurate, incomplete, duplicate, or redundant data needs to be removed and processed. In order to obtain accurate and reliable machine learning models, measures need to be taken to ensure the quality of the data.
