Inside the Briefcase

IT Briefcase Exclusive Interview: Getting the Most Out of Open Source While Managing License Compliance, Risk, and Security

IT Briefcase Exclusive Interview: Getting the Most Out of Open Source While Managing License Compliance, Risk, and Security

with Kendra Morton, Flexera
In this interview, Kendra Morton,...

Why DEM Matters More Than Ever in Financial Services

Why DEM Matters More Than Ever in Financial Services

Remember waiting in line at the bank? Banking customers...

How to Transform Your Website into a Lead Generating Machine

How to Transform Your Website into a Lead Generating Machine

Responsive customer service has become of special importance, as...

Ironclad SaaS Security for Cloud-Forward Enterprises

Ironclad SaaS Security for Cloud-Forward Enterprises

The 2015 Anthem data breach was the result of...

The Key Benefits of Using Social Media for Business

The Key Benefits of Using Social Media for Business

Worldwide, there are more than 2.6 billion social media...

Comparing 3 Popular Approaches to Data Labelling

June 9, 2021 No Comments

Featured article by Bernadine Racoma, Content Manager of eTranslation Services

numnum 300x200 Comparing 3 Popular Approaches to Data Labelling

In the realm of machine learning, the process of labelling data refers to a number of steps involved in assigning raw data identifying information, such as tags, names, and classes. It’s an essential step for any train AI models in predicting patterns. Unlike raw information without labels, tagged data allows for more precise or supervised ML applications. But how does it work? What are the steps involved in labelling massive amounts of information collected?

How does labelling data for ML work?

Now that you have a general idea of the purpose of labelling data for AI and ML applications, let’s move on to the process involved. The first step is defining the goal and preparing data sets. Usually, you can decide whether you want to put tags on a small portion of data sets that will be used in training AI technology.

Of course, the process also requires specialized software. The software used for this purpose will add labels only to areas you’ve highlighted or defined. Also, it’s vital to choose a software or method that will suit your defined problem or the type of information that needs labels. For example, there’s software designed to put labels on images, audio, and video. Right now, most AI developers use a variety of popular software and third-party services designed for data labelling.

Different sources of annotation

We can’t stress enough the importance of understanding that data labelling is a complicated process. It takes time and plenty of resources. Here is a brief look at the different annotation sources along with their pros and cons.

1. Outsourced. One common approach to labeling data is to hire a third-party service provider. This method is beneficial when you don’t have the know-how and resources to guarantee high-quality results. Although outsourcing means you don’t have full control, it will let you focus on more important tasks. Most enterprises choose to outsource because it’s efficient and cost-effective, and grants them access to the cutting-edge knowledge and expertise possessed by industry experts – without needing to go through recruitment. The benefits granted by outsourcing complex processes are shared

2. In-house. For more established companies, managing an in-house team can seem like the better option – after all, choosing to keep it in-house means maintaining full control over the process. However, the most notable downside to this approach is the fact that labelling a meaningful amount of data will require a considerable workforce – without which enterprises are ill-equipped to manage massive quantities of data. As a result, managing an in-house team will require investing in both human resources and infrastructure – a cost which can so easily spiral out of control.

3. Synthetic labelling. In addition to hiring people who will annotate data, synthetic labelling is another well-known method. One example is using GANs or generative adversarial networks. This process results in highly realistic, but fake data sets containing all the essential attributes of pre-existing unlabeled data sets. Since you’re using a program, it’s very efficient. But you’ll still need to invest in the right technology to achieve the best results. This solution is ideal for highly advanced tech companies.

Apart from these three sources, there are other options available. One is through crowdsourcing. Many opt for this solution because you can enlist top talent from around the world to work on the project. It’s not only cost-effective, but the industry’s competitiveness will also ensure you get excellent results by working with expert freelancers and data labelling professionals.

lala 150x150 Comparing 3 Popular Approaches to Data Labelling

Bernadine Racoma is the Content Manager of eTranslation Services. Her long experience in an international development institution and extensive travels have provided her a wealth of knowledge and insights into cultural diversity. She writes to inform, engage, and share the idea of the Internet being a useful platform for communicating, knowledge sharing, educating, and entertaining. You can find Bernadine Racoma at Google Plus, on Facebook and Twitter.

 

Image: https://unsplash.com/photos/hvSr_CVecVI

DATA and ANALYTICS 

Sorry, the comment form is closed at this time.

ADVERTISEMENT

Gartner