Scalability Bottleneck

Scalability Bottleneck

Manual Labeling

One major scalability issue we encounter is the repetitive nature of manual labeling. Each time new categories of data are added, new data is continuously accumulated, or when business requirements change, we are faced with the challenge of relabeling the data from scratch. This repetitive manual relabeling process hinders scalability and efficiency.

Programmatic Limitations

Another challenge we face is the limited transferability of programmatic labeling solutions across different sectors. While programmatic labeling may work effectively in a specific sector such as finance, it often falls short when we venture into other sectors like healthcare. The lack of transferability of labels from one sector to another poses a significant scalability bottleneck.

The Need for a New Labeling Approach

To address this scalability bottleneck, we require a new and more adaptive labeling approach. This approach should enable efficient labeling even as new categories of data are introduced, continuous data accumulation occurs, or business requirements evolve. We need a labeling solution that can seamlessly adapt and transfer labels across different sectors, eliminating the need for repetitive manual relabeling and maximizing scalability.

Finding a new way of labeling that solves these challenges will significantly enhance our data labeling process, improve scalability, and enable us to effectively navigate the ever-changing landscape of data requirements across various industries.