- Research for various types of publications and documents related to desirable data outcomes.
- Unbiasing the collected data. It means removing impaired, damaged data or the one from the suspicious sources in order to save and implement only trustful data in your Machine Learning model.
- Segregating the list of elements in a file type could be helpful for desired ML data outcome. For example, if you want data outcomes to be closely related to ecology, you should segregate the data files that dominantly contain the objects related to that(such as mountains, nature, garbage-free spaces, etc).
- The next is the labeling/annotating process. It is advisable to be done by a person experienced in that workflow so the data outcome can be more convenient and trustworthy.
- Incorporating the selected and labeled data into Machine Learning processing.
Building the model confidence is possible through tirelessly incorporating new, unlabeled data and testing it with some of the previous, familiar machine examples.
Some of the concerns are common for the majority of organizations, regardless of the level they operate within, who are struggling with AI and ML development-related projects are:
- Quality of dataset
- Workforce management
- Privacy of the data
- Financial obstacles
The success of the data labeling process is a big challenge for a workforce because the company’s imperative is to ensure high quality of the operating data while at the same time must manage and hire enough workers to handle a massive amount of unstructured data properly. That could be one hard, but a well-known fact to all companies that are struggling to stay competitive in a dominantly AI-supported market.