high volume data

4 Secrets For Managing High-Volume Data Labeling

What is the best way to grow your labeling workforce quickly, efficiently, and accurately, while still ensuring data quality for high volumes of data that seem impossible to manage? Human labeling task management is strategic and inherently connected to your success and failures, and the goal in all of this is maximizing your productivity. Maintaining or scaling your current large labeling workforce is the key to helping your team keep up with the enormous flood of unstructured data that arrives continually. But ensuring that a varied and large group of data labelers can deliver consistently high-quality data is the real challenge.

Today, I’d like to address challenges data project managers face in light of scaling their labeling workforce, and how using Dataloop optimally can help you manage a streamlined and effective data labeling workforce.

How to Practically Manage High-Volume Data Labeling Tasks

Secret #1: Centralize your data to collaborate with multiple labeling workforces simultaneously

Many AI teams in need of labeled data, use an external workforce that communicates via meetings, emails, etc. However, this process often finds you wasting precious time and only allowing you to start addressing errors/issues at the end of the process. Manually inputting data from different parties that have been changed by multiple vendors results in data that is not shared and becomes siloed. This process is highly inefficient, costly, and hinders an enterprise to scale its labeling operations. Centralization is paramount to allowing teams to work together, maximize their time, efficiency, and ultimately avoid unnecessary errors.

When everything is done from one central location, managing multiple labeling vendors simultaneously on a single labeling project who are independent of one another is much simpler. Imagine doing so from within a specific labeling assignment, for example, you can simply create a QA task and assign it to someone who is external on the annotation team. This ensures you’re using multiple independent people to review the data ultimately ensuring data quality.

Secret #2: Implement skill-based task flows

Optimize your task performance by working simultaneously on different annotation assignment types such as semantic segmentation vs. object detection bounding boxes, maximizing labeler knowledge, expertise, experience, or skill. You can distribute assignments to your workforce while taking into consideration the performance and proven skill of each labeler. You may have a lighter assignment involving classification and a heavier one like semantic segmentation, you can assign them to your annotators accordingly. Use your diverse workforce to plan your task progress with flexibility and keep up with the enormous flood of unstructured data that arrives continually.

Secret #3: Strategically weave ML models in order to assist your human workforce

Human intelligence is a crucial component of the supervised deep learning process with the increased need for human-labeled data as the model matures. Dataloop raises the level by implementing automation into manual human workflows, allowing teams to increase productivity by 90%. With features like AI-assisted annotation, pipeline automation, ML model integrations, and built-in AI trackers Dataloop augments human intelligence to increasingly automate the data preparation process, requiring human intervention for only model validation. This results in data teams accelerating their data throughput and dataset generation faster.

Secret #4: Leverage performance analytics for workforce efficiency

Maintaining visibility of your labeling workforce performance allows you to make informed decisions. Keeping track of individual performance, data operations, and task management metrics in real-time allows you to increase productivity. Utilize Dataloop’s annotation assessment tool in order to ensure annotator accuracy, which is referred to as the “Golden Set” or ground truth. Another option is to assign multiple annotators to the same task and conduct a consensus workflow. Compare the annotator’s work by comparing model inferences with the ground truth. This ensures each action your team performs on the Dataloop platform is measured and analyzed, helping you reach data-driven optimizations at every step of the process.

Summed Up

Task management can be much simpler once all is being done from one place. From the assignment of the tasks, the progress tracking, and the point of view of edge cases. Combat the high-volume influx of data by strategically using these secrets to manage your labeling workforce. As a project manager, Dataloop can help you gain control over the projects and make sure all goes smoothly without wasting your precious time and effort.

Is managing your workforce taking up too much of your time? Or, are you frustrated at how long it’s taking to annotate your data? Then set up your 1:1 session with our experts today.

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