Data collection is a process of collecting and storing data that is used for training artificial intelligence (AI) models. The collected data is used to train the model so that it can perform a specific task, such as recognising images or processing natural language. It is a critical step in the AI model training, and the data quality and quantity have a significant influence on the accuracy and efficiency of this process. It should be emphasised that data collection is a continuous process because the AI models need to be regularly trained to maintain their accuracy and keep up to date with new information.
data collection
The specific details of any given step will differ depending on the AI problem that is being solved and the source of data that is used.
The type of data needed for training will depend on the purpose of the AI model. For example, a machine learning model trained to identify objects in an image will require image data, whereas a model trained to predict share prices will need financial data.
Target data is the type of data on which the AI model will be trained for future predictions and classifications. In the case of the supervised learning model, this will usually be labelled data.
Collecting data from various sources, such as databases and publicly available data sets, APIs, sound recordings, pictures or web scraping. It is important to make sure that the data is adequate, accurate and of high-quality.
Collected data often requires pre-processing such as cleaning, normalisation and transformation to make it suitable for use in an AI model. This can be achieved, for example, by removing irrelevant or duplicate information and processing it (converting it into a format that can be used for training).
Annotating data with appropriate information, such as proper classification for the image recognition model.
Storing data in a format that is available and useful for the AI training process. It can be stored in a database or in a file format, such as CSV or HDF5 file.
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