In the world of machine learning, data is king. But data in its original form is unusable. That’s why more than 80% of each AI project involves the collection, organization, and annotation of data.
The “race to usable data” is a reality for every AI team—and, for many, data labeling is one of the highest hurdles along the way.
So, how do AI companies do it?
Why Is Data Labeling for Machine Learning Important?
A machine learning model is only worth the data used to train it. All of us who have studied AI have heard the saying, “garbage in, garbage out.” It’s true—to produce, validate, and maintain a machine learning model that works, you need reliable training data.
In machine learning, data labeling has two goals: accuracy and quality. Accuracy involves mimicking real-world conditions. How well do labeled features represent the truth?
Quality, on the other hand, refers to consistency. Have you maintained the same level of accuracy across your datasets? How do you make sure your data labelers adhere to the same standards?
How Are Companies Labeling Their Data Today?
Here are a few of the major approaches AI companies take to get the job done. We’ve outlined the advantages and disadvantages of each.
1. Relying on Your Own People
Your data scientists will be the ones working with the datasets once they’ve been labeled. Naturally, an in-house team has an intuitive understanding of what your project needs, plus the skills to accomplish the task at hand.
So, what are the downsides? The algorithms that power computer vision applications require immense amounts of high-quality data, which takes time to produce. Paying your data scientists to label images for machine learning isn’t just expensive—it pulls your best talent away from other ongoing projects.
All in all, in-house labeling guarantees quality but is much slower than any of the other approaches we’ll discuss today. But if your company has the manpower, time, and financial resources, it’s an option worth considering.
2. Entering the Crowdsourcing Marketplace
Crowdsourcing platforms like Amazon Mechanical Turk (MTurk) and Clickworker offer an on-demand workforce for data labeling services. Enlisting the help of online contractors typically produces speedy results, especially if your datasets are basic. To expedite the process even further, AI companies typically break their projects down into microtasks that can be assigned simultaneously.
Crowdsourcing is the cheapest route for data labeling. However, it often compromises both the quality and consistency of your datasets. Freelancers aim to get as much work done as possible, leading to inconsistencies. Unclear task instructions, language barriers, and faulty work division can also lead to poor quality.
3. Outsourcing to Specialized Data Annotation Services
Another option is to partner with a company that specializes in data annotation. Rather than relying on valuable employees or overseas freelancers, consider outsourcing to the data labeling experts.
Data annotation companies are well-equipped to tackle high volumes of complex data. At Keymakr, our annotators are all authorities in machine learning themselves, and are well aware of what it takes to train a high-performing model. We take the time to learn the scope of your project and its objectives. You can rely on us to deliver the best results on time.
Data Annotation in Machine Learning: What Are the Challenges?
Do you have low-quality data? The quality of your data stems from the people, processes, or tools involved in structuring and labeling your datasets. At Keymakr, we specialize in custom annotations that guarantee superior quality and consistency—exactly what you need to keep your algorithms happy.
Machine learning algorithms require immense amounts of data. But scaling can be difficult and expensive for companies with limited resources. Keymakr offers professional annotation services for projects of any size and complexity.
Data labeling is inefficient and prone to errors when using the wrong tools. Our industry professionals use advanced tools to not only label, but to collect and create data as well.
Do you have unique data needs? Does your machine learning model call for millions of specific images or clips? Our tailor-made solutions have got you covered.
Outsource Your Data Annotation the Right Way
Computer vision has unlimited potential, from unmanned drones to robust facial recognition software. But the performance of every computer vision application can be reduced to the quality of its training data.
Are you interested in high-quality image- and video-based training datasets delivered by the experts? Get in touch with the team at Keymakr today for pixel-perfect results.