Reason Why Business Must Outsource Data Annotation in Machine learning

Building an AI/ML model requires large volumes of training datasets. Companies developing their machine learning algorithms have to make an important decision: whether to have an in-house team or collaborate with an established outsourcing partner. So, this piece tells you why the data annotation in the machine learning process is better left to professionals.

Organizations leverage AI/ML applications like Computer Vision, Natural Language Processors, Deep Neural Networks, etc., across various business functions to increase their efficiency. And for these smart models to act like humans need supervised training done via the process called data annotation.

Data Annotation in Machine Learning – Introduction

It is the process of adding labels to the input datasets that have to be fed into the machine learning algorithms. These labels are added in the form of tags, descriptions, meta tags, and so on. This information helps the AI/ML models to understand their environment better and deliver reliable results.

However, AI/ML adoption is not as easy as it sounds and labeling input datasets accurately is a challenging task. Errors or redundancies in this process deviate from the desired outcomes. Remember, any AI model is as good as the data it is fed with.

Though hiring an in-house team offers you advantages like more security, direct oversight, and better production for IP, it is not the only option. The process of data annotation in machine learning is often complicated, expensive, and time-consuming. It poses logistical challenges for many companies as all of them can’t redirect the necessary resources and time needed to manage a team of data labelers. Adding to this, the infrastructure required and the need to develop the right tools and software costs swiftly spiral.

Besides, the requirement doesn't always remain the same. Even a dedicated in-house data annotation team might not be able to manage large volumes of data within the stipulated. They may or may not be agile enough to manage complicated datasets. Outsourcing data annotation services is, therefore, a smarter way.

Outsourcing Data Annotation – Benefits

Working with an established data annotation company helps companies to save their resources without sacrificing quality. They have a pool of data professionals, competent annotators, and Subject Matter Experts (SMEs) who ensure that each data set is labeled accurately. These professionals are equipped with cutting-edge technologies, robust workflows, and automated tagging tools. The right blend of skills and experience ensures excellence in every outcome. Apart from professional excellence and technological advantage, businesses enjoy benefits as mentioned here:

• Scale

AI/ML projects need a bulk volume of labeled training datasets to be successful. Though the goals of these projects can vary in complexity, they have a common requirement, that is, a large volume of high-quality data. Many organizations do not have the existing resources for large-scale data annotation projects. Also, pulling in engineers and other labelers to perform data labeling tasks is an expensive affair.

To cover the data your AI/ML might encounter in the real world, offshoring can provide a pool of qualified professionals to perform these tasks whenever required. They offer the ability to adapt and scale according to the data annotation project requirements without losing data quality. On the other hand, internal annotation teams may not have the bandwidth to handle shifting project needs or required experience.

• Quality

Ensuring accuracy and quality of training data are critical to the success of a machine learning model. A great advantage of outsourcing data annotation in the machine learning process is that experienced professionals perform these tasks. They work much faster and more accurately. The experts have instructional guidelines and are equipped with purpose-built tools for data annotation. They are accustomed to labeling large volumes of data. This implies they can maintain speed and assure a high level of accuracy.

• Security

Data security is one of the important factors for businesses outsourcing their machine learning projects. Data annotation outsourcing is a task due to data privacy concerns including compliance such as PII or PHI, GDPR, or other sensitive data-related considerations. Therefore, the outsourcing companies offer multiple service delivery offerings such as ISO-certified secure facilities, secure work-from-home data annotators via VPN, on-site workers working within the customers’ proprietary tools, and so on.

• Speed

Relying on an in-house team for data labeling might delay the completion of the project. Training these employees to help them understand the project’s goals takes time. So, if the project lacks urgency, slower time to completion may be acceptable. But when you have to compete in the market space, time must be utilized strategically. Outsourcing annotation requirements to an impeccable vendor means the difference between weeks and months.

Bottom Line

Hiring an in-house team for data annotation in machine learning is beneficial for small and simple projects. To help ensure success, collaborating with professional providers is the best way. They develop enhanced training sets within the stipulated time and are the right choice for organizations, irrespective of the industries or verticals they deal in.

At Damco, the professionals understand the AI/ML model’s potential use case and thus label input data sets accordingly, thus helping businesses to fuel their smart models.