Artificial intelligence

Data Labeling Outsourcing Guide for AI Companies

Data Labeling

Conquering AI horizons is now even harder than it was in the very beginning, when artificial intelligence was considered a science fiction. Sophisticated, AI-driven solutions are permeating nearly every aspect of our lives like Data Labeling.

More AI, though, requires more data that underpins these tech solutions. Say you are working on the new project — a face recognition system for a large enterprise. First, you need to train the model to recognize human faces by feeding it with a decent amount of labeled training data. Now the question is, where to find the most perfectly annotated datasets?

Outsourcing is the tried-and-true method of obtaining labeled data for your project. However, the competition between the data annotation companies is escalating. We therefore prepared a thorough data annotation outsourcing guide for AI firms to aid them in exploring all AI frontiers and bringing technology to the masses.

Why Outsourcing is Your Go-To Solution in AI?

According to IBM research, a little more than half of businesses are benefiting from automating network, business, and IT activities with AI. Among these benefits also are reduced costs and efficiency (54%), enhanced IT performance (53%), and improved customer experience (48%). Outsourcing plays a fundamental role in these statistics, since each of the benefits and numerous AI applications involve tons of data. It needs to be properly processed and put into work.

Rather than building your own team of data annotators and dealing with all the nerve-racking work it entails, most AI companies today prefer to outsource the most tedious and resource-intensive tasks. One such task is data labeling. It’s a crucial part of the model training and building a whole new AI system in general.

Why do these companies choose outsourcing? Data labeling outsourcing is a smart move if you want the job to be done good, fast, and at a lower cost. However, Deloitte states that “cost is no longer a differentiator” in AI, which is why modern data annotation companies start to lose the cost edge they once had. What matters now in any data-related initiative is a high-end service. Yet, with many benefits come the drawbacks. The rising number of companies providing data annotation services, finding the best outsourcing partner can get tricky.

But we have a solution!

7 Things to Consider When Outsourcing a Data Labeling Project

Source: PwC

As they say, where there’s a will, there’s a way. The same is true for any undertaking in AI. If you need high-quality labeled datasets, you are determined to find the most trustworthy and professional data labeling service provider. Because you don’t want to feed your machine learning model with poor-quality data that will completely undermine the end outcomes.

Don’t be fooled by how simple the data annotation process seems. It’s a challenging and intricate task that requires a strong attention to detail and understanding of what data actually is. Therefore, you need to find a team of true data experts to take on your data labeling project.

When outsourcing, keep the following things in mind to get the most perfect annotated dataset for your AI project:

  1. Data security & privacy

The majority of client data is sensitive, and so it needs to be properly handled and protected against unauthorized access. A reliable data annotation company is the one that maintains privacy of your dataset. You can read the article to know how this should be done. A data labeling company must be compliant with GDPR, CCPA, and other regulations for data protection and privacy. So, you won’t have to install any add-ons or use extra tools to follow the data privacy regulations.

  1. NDA agreements

One of the responsibilities of a competent outsourcing provider is to protect the client’s information. Today’s advanced technologies for annotating images, texts, or audio data come with access-setting capabilities. They make it easier to manage corporate data. Besides, top data annotation providers also sign NDA (non-disclosure agreements) to guarantee confidentiality to their client. Also, checkout this Technology Blog.

  1. Team & communication

Meeting your new team is always exciting. But it won’t be an issue if the communication with the remote crew is well-organized. Both annotators and their managers should be responsive to your requests and possible changes throughout the project. This means regular updates, feedback exchange, and timely delivery of the task. Additionally, choosing the appropriate channel and time for communication between your team and the annotation team will help you both stay proactive and attentive to the smallest details.

  1. QA procedures

Quality assurance in data labeling defines the accuracy of the annotated datasets and, therefore, the quality of the model training process. The labeling process cannot be complete without QA. For the ML model to perform well, the labels on the data must indicate a level of accuracy that is close to the ground truth, be distinct from one another, and serve some purpose. The precision of labels for particular data points is measured by the common QA procedures. They include the Consensus algorithm, Cronbach’s alpha test, benchmarks, and reviews.

  1. Speed vs quality

If your company is working on a specific AI project, there are many crucial processes and tasks that require due attention, time, and effort from the entire team. This is why companies like yours choose to outsource data labeling. However, we advise you don’t be dazzled by the speed an annotation company might offer. Because the speed of data labeling has nothing to do with the quality of the annotations. Also, remember that data is the most fundamental mechanism within the machine learning pipeline, and so the quality of labeled data is equal to the level of the model performance and credibility of the outcomes.

  1. Cost

After you figure out all of the above things, you’ll probably want to discuss the financial issue. Outsourcing is not only a way to save your time and resources, but also your money. When outsourcing to a data labeling provider, the cost directly depends on the data volumes that need to be annotated, the type of annotation, the number of annotators to be involved, etc. Each project is unique, and so are the requirements for each task. The cheapest option in the market would not be your best bet because, as the saying goes, don’t be penny wise and pound foolish.

  1. Labeling software & tools

Data annotators use various kinds of software and tools for high-level accuracy and the most efficient results. When in search for a data annotation partner, check their software options. A company might also have their own labeling platform. The sophisticated labeling tools that skilled data annotators use can satisfy the needs of any AI project. Controlling the process shouldn’t be a problem because top software solutions include capabilities like reporting, QA, and tracking. However, you can always ask the company to use your in-house tools to have a better control of the process.

On a Final Note: The Outsourcing Process Inside Out

Photo by ThisIsEngineering on Pexels

The data labeling procedure can be a significant load on your internal team, your business operations, and resources. For this reason, most AI companies find outsourcing annotation services to be the most suitable option for their business. Yet, outsourcing is a tricky skill to be learned and developed over time.

Because each piece of annotated data builds a complex system of machine learning algorithms, the labeling accuracy determines the success of your project. Clients require professionally labeled data to bring their AI projects to life. Therefore, selecting a reliable data annotation partner is crucial not only for your data, but also for the entire project.

With so many data labeling outsourcing options in the market, we hope this guide helps you achieve the most successful partnership in AI!


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