Mangesh

  Challenges in Data Labeling Workforce Management Data labeling is itself a challenge! Think about it. Tons of unstructured data ready to put up a name slip on them while segregating them into categories, species, diversity, etc! ML and AI models need Big data. To handle and label this big data you need to magnify your workforce. Not only quantity-wise but also quality-wise. This is a definite challenging task for budding businesses. There are a lot of tasks left to be performed that get delayed due to data annotation.  Hiring people and then training them, seems like a forever task. Further, the data set quantity increases every day. Inhouse data labeling can be detrimental at times! Manage Quality flow   If you are rooting for the best results, a quality data set is your asset. You have to build up your model on quality resources. Maintaining consistency while ensuring data quality is also a must. Thus, maintaining quality consistency is a challenge in data labeling. B...