The global market of Data Annotation tools is expected to grow at 15.1% CAGR from 2021 to 2030. This fast-paced growth is due to the high digitization of consumer products and services. Starting from facial recognition in smart devices to online rendering services, every step towards digitalization brings in more demand for Data Annotation tools. Also training the AI models using Data Annotation tools has seen a surge in the demand for IoT.

Deploying efficient human intelligence needs to be trained by experts of AI and ML. To achieve a robust Artificial Intelligence performance, you need to go through several experimentation and optimization cycles. By leveraging advanced AI systems and mature data labeling infrastructure, you will be able to train your data consistently and accurately. However, there are considerations to be made before you build or buy your Data Annotation tool. 

Below are few considerations to follow:              

• Right Time to Market

Reaching production is a crucial consideration for your AI application, and adopting a purpose-built platform might get you to value quickly compared to creating an internal solution altogether. The opportunity costs are also ignored as they might be rather expensive for the organization. 

• Annotation and more

Data labeling tools that are created internally are mostly built for short-term purposes. It does not quite fulfill the parameters like scalability, usability, and cross-team support. Therefore, the key to attain high-quality training data is to have QA/QC capabilities which also includes consensus, task queuing distributions, and reviews workflow. To have all these you need to invest a significant amount of money, apart from just annotating data. 

• Actual Benefits

You need an advanced Data Annotation tool that has proven customer success as well as increases your tendency to realize similar benefits. A Data Annotation tool is built, scaled, and tested based on multiple cases which yield more value than just a solution.

• Cost of Ownership

Internal tools are often built to serve a specific function with new business demands, which tend to cover the future costs of enhancement expenses. Added with the high costs of ongoing maintenance, technical debt increases with time. All this is due to developing product demands, engineer turnover, or product neglect. 

• Evolving Opportunity

You need resource allocation, planning, and preparation for the future while developing an internal product. As training data platforms are new, it is difficult to measure the scope and build a tool for its requirements across the product and engineering teams. 

• Annotation of Data is Cross-functional

Turning raw data into accurate and consistent training data requires a Teamwork. A team that comprises of engineers, domain experts, managers, and more people playing distinct roles. Data Annotation must provide information and unique interfaces for every role. 

• Security is a Big Factor

Buying a Data Annotation tool lets you define your security protocols and expertise from a third party.

So, the decision of buying or building a Data Annotation tool is up to you or the  organization. However, investing time and energy in exploring the aspects as discussed above would help your organization in realizing it to its core.

Also, if you are considering a Data Annotation platform to partner with, Data Labeler would be your best support.

How Data Labeler can help you with advanced Data Annotation Tools?

At Data Labeler, we combine our expertise human-assisted approach with Machine Learning assistance to provide you with the best quality training data. We offer cost-effective solutions for high-quality labels which undergo constant quality checks. 

Why choose us? 

We provide extended support to make sure your datasets meet your expectations. Also, we do not have any minimum purchase limit. Contact us at Sales@DataLabeler.com

What are you waiting for?  Partner with us for Data Labeling Services or Become a Reseller.