Data Annotation

Data Annotation and Data Labelling works together to prepare dataset for Machine Learning Models in Data Science practice.

‘Data Annotation is the technique of doing Data Labelling.’

Data Annotation is a complete process with many steps to curate, whereas Data Labelling is ONE of the many steps in it.

Data Labelling is about tagging the data for training e.g., machine learning models whereas Data Annotation is the technique for tagging the data so ML models can reuse it as well. Data Labelling tags are used by ML models for future pattern recognitions whereas Data Annotation is about recognizing the data via a process.

Just like Data Labelling, Data Annotation is also for supervised machine learning to prepare data as an input and to store it for future reference. Not to mention, there are STILL many organizations doing manual Data Labelling. Data Annotation is about the process so Data Labelling can be automated for accuracy, eliminating human intervention. Bad Data Labelling can impact the outcome of applications.