7 Signs that your AI data labeling operations need an upgrade

Introduction

Imagine running a business without artificial intelligence today - you would either have a 7-day workweek or no business at all. But for AI to work seamlessly, your Machine learning models and thereby, your data labeling operations need to be efficient, accurate & seamless too. Data labeling operations, the often ignored cousin of ML ops, is the key to having your business AI-enabled.

What are the stakes for data labeling in an AI-led world?

As more and more businesses are stepping into the Artificial Intelligence-led automated world, the demand for labeled data to train ML models is also on the rise. Without effective data labeling, it will be next to impossible to deploy AI models that are capable of accurate understanding based on real-world conditions. For any machine learning model to give the ideal output, the need for clean data cannot be put into the back seat.

The majority of enterprises tend to rely on small in-house teams or crowdsourcing initially for getting the job done. While it may seem feasible and convenient initially- chaos soon follows.

Considering the shortage of proficient labelers in such teams, it becomes hectic to meet the high-volume labeled data requirements. In the crunch, there's hardly time for cleaning the data and verifying its source, which ultimately affects the data quality and brings down the company's ROI. Thus, only having labeled data to the rescue won't be of much help because on-time scaling of data labeling operations is also important. Here are 7 tell-tale signs that your data labeling operations are due for an upgrade:

Signs that your data labeling operations need an upgrade

Low quality of labeled data

As the majority of enterprises nowadays are data-driven and rely solely on AI for the majority of data assets - their struggle with low-quality data is constant. As a result, such datasets can affect the ROI of the company and even lead to higher operational costs. However, the reasons behind the low quality of the labeled data can be many - some prominent ones are,

• Duplicate data
  As the data sources are many in an enterprise - from cloud data lakes to data silos and local databases - the chances of overlapping are higher, which often leads to the duplication of data. Frequent data duplication doesn’t only affect ML models but can result in a negative customer experience.
• Data downtime
  As the majority of enterprises rely on data to make decisions now - there are instances when the data is not ready due to migration issues or the data source might not be reliable to use because of schema changes. These issues are common in modern enterprises and ultimately lead to data downtime and end up stretching the data pipeline.
• Inconsistent data
  When teams work with data from multiple sources, inconsistencies like mismatched information and format discrepancies are bound to happen. Even if these bumpers can be avoided, the chances are higher during data migration and mergers. If such inconsistent data piles up, it can bring down the overall data quality.

Lack of finding trained labelers

Finding data labelers who are experienced and qualified enough, is hard to come by and it's even more challenging if your project falls into a specialized niche. If you rely only on your in-house team to meet such specific requirements, the data quality might degrade because your team might not have ample experience or training. Thus, outsourcing such top-notch labeling tasks seems to be the ideal way out.

Inability to scale data labeling operations

As AI proliferates enterprises, the need for labeled data only seems to grow every day, and to meet the ever-growing demand, it’s important to scale your company’s data labeling operations. As the majority of organizations have in-house data labeling teams, meeting the data volume requirements while keeping the quality in place - seems impossible. The solution, however, is to upgrade to a human-in-loop system along with advanced techniques like active tooling, automated benchmarks, and sensor fusion for delivering high-quality annotations without compromising the volume.

Unbearable costs and non-existent results

26% of enterprises failed at their AI projects only due to the lack of enough budget. The scenario would have been much different if there were scopes of better monitoring and financial transparency between these smaller teams of larger enterprises. The major budgets of organizations go behind hiring highly-paid data scientists and AI professionals or building in-house teams with a group of amateurs or crowdsourcing to labelers who usually charge by pay-per-task pricing module.

However, all these measures can backfire quite easily because

• Building an in-house team with amateur labelers who aren't adequately trained can hamper data quality
• Crowdsourcing to labelers who charge by pay per task might deteriorate the data quality due to the rush of finishing more tasks faster

Poor quality assurance

Labeled data cannot be used if it has poor quality assurance. The keypoint annotations, precision of tags for a particular data point, and accuracy of the coordinate points for bounding boxes are used to measure the quality of labeled data. To ensure better accuracy, putting quality checks such as the Consensus algorithms, Cronbach's alpha, benchmarks and reviews can make a significant difference.

Not using tools to automate workflows

Enterprises have always prioritized human intelligence when it comes to training datasets for AI models. Even though we no longer annotate data manually, labor-intensive tasks like manually allocating tasks, designing workflows, and mapping performances affect your team's productivity by slowing down the whole process.

Unreliable delivery times

With a small internal team of labelers or even disparate teams in-house & outside -  the overall performance may deteriorate when labelers quit their job without notice and new hires onboarded are yet to be trained. It ultimately leads to data downtime, which might incur revenue loss for the company.

But outsourcing this only is not the solution. Managing disparate data labeling teams, and data types, improving the learning curve & productivity of labelers are all factors that decide if your labeling ops scale sustainably like a plant or burst like a balloon.

The Taskmonk Advantage

With Taskmonk, you get a multifunctional collaborative labeling platform that can work with labeling applications for all data types and centralize procurement of the labeled data across diverse teams-internal & external for faster, seamless AI execution.

Here is how Data labeling with Taskmonk would look like:

1. Affinity-based task allocation to speed labeler productivity
2. Handle multiple data types in a single platform
3. Active learning models to increase labeler efficiency
4. Robust reporting and analytics module to generate detailed insights into the labeling process
5. No Code Workflow creator to decrease the labeler learning curve, without involving Data Scientists

In short, you can stay on top of your data labeling process, collaborating with diverse teams, and improving process & labeling efficiency, without breaking in a sweat.

Want to know how? Reach out to us at info@taskmonk.ai for a quick call with our experts!

Conclusion

In many cases, companies choose to ignore the signs of the deteriorating performance of their data labeling operations because of the high cost/tool/team/time investment in their current labeling ops methods in hopes of eventually figuring it out. While feasible, this comes at a high cost that can be devastating - not only monetarily but also for opportunities lost to the competition and end-user experience. And that is the last thing any business wants today.