Machine Learning is fueled by data, and in most cases, that data is labeled manually by humans.
Data labeling for machine learning has spawned a totally unique industry. The organizations springing up to help companies label their data are between the most ardent “picks and shovels” investment plays for enterprise investors aspiring to cash in on the modern A.I. gold rush.
The latest data point in this data labeling business: Zuru, an India-based startup that provides end-to-end scalable data annotation solutions, is helping companies manage their data labeling tasks with stellar accuracy and swift turn-around time.
Affording labels can be a comparatively low-skilled job (identifying “dogs” in images) delivered by thousands of service providers in popular outsourcing centers like India, Romania, or the Philippines.
Often organizations require both general and more skillful labeling and employ a combination of outsourcing firms, freelancers, and in-house specialists to join these annotations.
The labels can be of different types, such as drawing bounding boxes around items, tagging objects visually or with text labels in pictures, or listing a group into a separate text-based database that supplements the original data.
Why Do We Need Data Labeling Services?
To train your machine learning model, you need to go into the camera footage and mark up all the required objects in the photo. The machine is then fed with this labeled data with hundreds and thousands of these images to correctly identify the entity.
With time, the system gets more accurate by being fed with more and more accurately labeled data. As this method is crucial for machine learning algorithms to precisely perform core parts of their function, the data labeling business has been at a boom since the preceding five years.
This is when data-labeling startups come into action. These companies are particularly imperative for A.I. and ML service providers that deal with object and image recognition, autonomous vehicles, and text and voice annotation.
But first, let us see some of the challenges faced during the data labeling process.
Knowing the cause of specific data labeling challenges is the initial step in solving them and enhancing M.L. project success rates.
#1. Struggling to handle a vast workforce
Training many laborers for their task and distributing work seamlessly across the large and varied team can be tiresome. You need to track individual progress without losing track of the project as a whole and ensure continuous delivery and collaboration among labelers and data scientist(s) to maintain quality checks, validate data, and fix workforce issues.
#2. Controlling the cost of Data Labeling
Many organizations struggle to manage their budgets correctly for data labeling, and when asked, 32% of enterprises blamed a lack of funding behind the failure of their A.I. projects. If companies choose to outsource data labeling services, they get the option to pick between paying per hour or per task. Spending per task is comparatively more cost-efficient.
#3. Falling short on consistent and quality data tagging
A quality-labeled dataset is what every organization is looking for. They need to find ways to ensure that labelers have the appropriate skillset to deliver consistent dataset quality. Outsourcing to an experienced service provider can guarantee consistency while ensuring data quality.
How ZURU is bridging the gap between quality data labeling and machine learning models
Companies in the labeling space, such as Zuru, provide labeling services to generate annotations for several kinds of data. They are offering excellent data labeling accuracy for any use case in computer vision. Their team of experts works on complex computer vision algorithms with elaborate edge cases and taxonomies.
With expertise in 48+ languages to process data for NER, document processing, sentiment analysis, and more, Zuru has annotated 10 million data points in industries ranging from retail to BFSI to healthcare.
With NLP annotations covering sentiment, pitch, intent, timing, transcribing, and more for speech AI, Zuru’s team has done it all.
Zuru has a fixed structure which they like to follow:
- First, they understand your requirement and analyze sample data.
- Next, they develop workflows, execution plans, and end-to-end processes before starting the annotation task.
- Then they label the sample to run it on micro models. It is then prepared to scale.
- Now they mark at scale with their trained teams, robust feedback loops, and constant quality checks.
- At last, precise and accurately labeled training data is all yours.
Zuru can serve as a single source of truth for training data across an organization. With the rapid growth of data labeling service providers, Zuru is the potential data labeling startup you are looking for.
Mrinal Walia is a professional Python Developer with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence, and Computer Vision. In addition to this, Mrinal is also a freelance blogger, author, and geek with four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Freelance content writer and content analyst.