Data acquisition is the stage in the AI project cycle where data from multiple sources is collected, cleared and aggregated in a suitable format and normalized.
This is an important step as the quality and quantity of data will greatly impact the performance and accuracy of the AI models developed in later stages of the project.
Data can be collected from a variety of sources such as databases, sensors, and external sources such as social media or publicly available datasets.
This data must be cleaned and preprocessed to ensure that it is in a format that can be used by the AI models in the next stages.