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In machine learning, data annotation involves the labeling of data to illustrate the outcome that is to be predicted by a machine learning model. One needs to mark, label, tag, transcribe, or process a dataset with the features that are required by a machine learning system to learn to identify. Once the model is deployed, it can recognize the features and make a decision or take action.

The annotated data depict features that train algorithms to identify the identical features in the data that are not annotated. Data annotation is implemented in supervised learning and hybrid machine learning models that comprise supervised learning. Data annotation has a key role to play in the machine learning space. It forms a fundamental aspect in the success of an AI model, as the only way to detect a face in a photo through image detection AI is if photos already labeled as “face” exist. 

Keeping the Labeling Game Strong

Data annotation is primarily the method of labeling data so the machine can understand and learn the input data utilizing machine learning algorithms. Data labeling or data tagging attaches some meaning to various types of data to train a machine learning model.

The key purpose of annotating data is labeling the data. To label data, consistent naming is a prerequisite. Sometimes, after training a model on the data, one can discover that the naming convention was not adequate to create the needed predictions or ML model intended. This necessitates the need to get back to the drawing board and redesign tags for the dataset.

But, Is “A” for Automated Annotation = “A” for Accurate? 

The answer is not a clear yes. Here’s why.

Some data can be annotated automatically, or, at least with the help of automated means with a certain degree of accuracy. Below is an instance of a simple form of annotation:

• Searching an image of an elephant via Google and downloading the top 1,000 pictures into an elephant file.

One collects elephant data automatically, but the degree of accuracy of the data is unidentified until examined. Also, it has a probability that some of the downloaded elephant photos are not actual photos of an elephant.

Automation aids in saving costs but risks the degree of accuracy. On the other hand, human annotation is costlier but more accurate. The data annotators annotate the data to the specificity of the collected knowledge. If it is an elephant picture, a human can approve it. Further, if that person is an expert in elephant breeds, they can further annotate the data to the specific breed of an elephant. Also, the person can draw a certain shape around the elephant in the picture to exactly annotate which pixels comprise the elephant.

Ultimately, the data is annotated to both:

• Degree of specificity
• Degree of accuracy

Which is more essential is always dependent on how the machine learning challenge is defined.

So, Can You Choose the Appropriate One from the Pool of Tools?

Data annotation tools that are used to enrich data for training and installing machine learning models can indicate the accomplishment or failure of the AI project. These tools play a crucial role in both, creating a high-performing model to power a disruptive solution or solving a challenging, expensive problem.

Choosing a perfect tool is not a quick or easy decision. The ecosystem of data annotation tools is transforming drastically as a greater number of providers offer possibilities for an increasingly varied array of use cases. Further, the tooling advancements are carried out monthly, sometimes weekly as well. Such changes help bring enhancements to existing tools and new tools for the existing use cases.

The challenge is to strategically think about the tooling needs at present and from a future perspective. Factors such as more advanced features, new tools, and variations in options, such as storage and security options, make tooling choices more complicated. Additionally, an intensely competitive marketplace makes it further challenging to determine hype from real value.

What Does the Future Market Scenario Annotate?

According to a recent report published by my company, the global data annotation tools market size was valued at $494 million in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 27.1% from 2021 to 2028. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and health care sectors. Further, the expanding ecosystem of big data and the rise in the number of large datasets are likely to necessitate the use of artificial intelligence technologies in the field of data annotations.

Owing to the rising scope of growth in data labeling, companies that develop AI-enabled health care applications are collaborating with data annotation companies. For instance, in November 2020, Telus International, a provider of digital customer experience (CX), and digital IT solutions and services announced to acquire Lionbridge AI, which offers training data and annotation platform solutions used for designing AI algorithms to power machine learning.

The escalating R&D spending towards improving image annotation for pushing developments in the field of self-driving vehicles is boosting the growth of the market. For instance, in January 2021, TCS announced the launch of an autoscape solution set for autonomous and connected vehicle ecosystem players that comprised automotive OEMs, suppliers, start-ups, and fleet owners. The solution addresses technology and business challenge and provides services such as petabyte data collection and analysis, validation, and deployment of algorithms, which offers proper guidance and control autonomous vehicles in the real world. Such developments are creating new growth avenues for the data annotation tools industry.