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Workflow
three key workflows are in the training process of AI models: data extraction, data filtering, and dataset management.
Data extraction is the starting point of the AI training process, which involves obtaining raw data from various data sources. These data sources can be public websites, databases, social media platforms, etc. Data extraction tools can automatically collect data from these sources, whether it is static web content or dynamically generated data.
Data filtering is a key step in ensuring data quality. After extracting a large amount of raw data, it may contain noise, irrelevant information, or even erroneous data. By applying various filtering techniques, these unwanted parts can be removed to retain data that is valuable for model training. Common filtering methods include rule-based screening, using machine learning models to identify and exclude low-quality data, etc.
Dataset management is the process of organizing extracted and filtered data into a structured format suitable for model training. This includes operations such as data labeling, classification, balancing, and format conversion. A good dataset management system can ensure the diversity and representativeness of the data and avoid overfitting of the model due to data bias.
Specific web scraping solutions
Computer vision applications : When training models to recognize and classify images, web scraping helps gather lots of image data. For example, we can scrape product pictures from online shopping sites and photos shared by people on social media. This helps train computer vision models to recognize different things, places, and actions.
Natural Language Processing (NLP) model training : Web scraping provides a large amount of text data for NLP models. For example, by scraping content from news sites, blogs, and social media platforms, models can be trained that can understand a variety of language styles and topics. Language models like ChatGPT use lots of different text from web pages to learn grammar, meaning, and context.
Public opinion monitoring and sentiment analysis : By collecting user comments and posts from social media, forums, and news sites, AI models can learn to understand what people think about specific events or products. This sentiment analysis model is of great value for brand management and product updates .