Answering Key Questions About Getting Started With Data Annotation

According to a 2020 report on the state of AI and ML, organizations revealed that they were using 25% more data types than the previous year. With such greater differentiation in data types, the need to increase investment in understanding and producing reliable training data followed. In fact, as per experts, the human race may be a long way from realizing the full potential of data through AI, but we are getting there. You may have noticed that self-driving cars have moved farther away from science-fiction and more towards reality.

Want to know the science behind this success? It’s DATA ANNOTATION! This technology helps train

the AI to identify other vehicles on the road and foot traffic coming in on red lights.

Let’s try and answer the basic questions so you can get started with data annotation platform, like what it is, why you need it, and more.

• What is Data Annotation Exactly? And Why do you need it?

Simply put, annotation is the secret ingredient that helps build a defensible data stack and supply an ML model with what it needs to understand and analyze various data inputs to produce accurate outputs.

By feeding tagged and annotated data to an algorithm, you’re basically helping the model get smarter. The more annotated data you use, the smarter your AI/ML model becomes; it’s that simple. But the question is, how much data do you need to annotate?

• How Much Data Do You Need to be Annotated?

Quite honestly, no one can possibly answer this question. However, in some instances, certain benchmarks can be established based on the project’s requirements. For instance, if you need the past 5 years of data for self-driven vehicles on the road. Once that’s established, a domain expert can handle the annotations and continually evaluate the model’s accuracy, helping create reliable data that will be used to train your algorithm.

• Do You Need It for the Initial Model Launch or Production?

There are two types of data annotation - the annotation of training data for an initial model launch like a new self-driven car or annotation for a model already in production. While the process produces similar output, by nature, they are quite different and require a distinct approach.

For instance, if you are annotating data for a new model launch, the data set is usually large and one-dimensional, making it easier to annotate. On the other hand, if you annotate data for an existing model, there will be operational constraints as the existing model is already serving your customers. So annotations need to be done in-the-loop or offline, which simply means before the customer sees the model.

This is why first, you need to determine the purpose for annotating data.

• Do You Need Subject Matter Experts or Not?

Irrespective of the complexity of the data you are annotating, you need the right expert on board. Several companies leverage data annotation solutions for basic annotations. However, where more complex data is involved, a specialized professional with market-ready skills is essential to ensure accuracy.

So you can either invest in data annotation solutions and hire a team of in-house experts with expertise in the domain or outsource the entire process to data annotation service providers, which brings us to the last, most important question.

                                          

• Should You Outsource or Set Up In-House?

According to a report by Nasscom, the global spend on outsourcing and third-party data annotation solutions is estimated to reach 7X by 2023 compared to 2018.

Not only this, organizations believe that internal data labelling costs them 5X more than outsourced data annotation services or solutions. In addition to this, the process is tedious, time-consuming, and labor intensive. This means, if you do decide to set up an in-house data annotation platform, you’ll need to assign a plethora of resources in terms of money, time, and personnel to produce high-authority annotated data. 

So yes, it is ideal to outsource the entire process to data annotation services. FiveS Digital - Your Data Annotation Partner in Need

FiveS Digital, an expert in data annotation solutions, can help you reach maximum efficiency at every level of your business. By curating the perfect balance between human and machine intelligence, expert data annotators at FiveS eliminate unnecessary processes, duplication, and incorrect inputs, ensuring your data preparation never stops. 

So if you are striving for a reputable and trusted data annotation platform or solution, Data Preparation Service, FiveS Digital can be the ideal partner. Contact a FiveS expert and make your way to high-quality data annotation TODAY!