Truly scalable data labeling starts with experience

Quick, no-hassle data labeling at your service

Artificial intelligence (AI) systems are effective only if they’re trained on quality data. Before that can happen, we need to gather enough relevant raw data and accurately label it – a long and potentially costly part of any AI project. But it doesn’t have to be.

It’s understandable that some companies choose to keep data labeling in-house, thinking they’ll save time and money while keeping a close eye on the quality of the work. But, if you consider that data preparation accounts for more than 40% of all efforts in any AI project, is this a wise decision?

Having high-salaried software developers or machine learning (ML) engineering employees spend countless hours labeling data means their daily tasks will be neglected, affecting productivity within the company. Data labeling rarely requires data scientists with PhDs – in fact, anyone with good analytical skills can be a promising candidate. So, let your employees focus on the tasks they’ve been specifically hired to do.

Another option is to hire people and build your own data annotation teams. Although this is usually less expensive, it’s time-consuming and requires project expertise. Who will be in charge of ensuring they’re properly trained to accurately label your data? If the teams are dispersed, can you really guarantee consistency in the quality and speed of service delivered? And more importantly, what will happen if you suddenly require more or less of their services?

The sensible approach is to enlist the help of a service provider that already has a managed global annotation workforce, from which the right domain experts can be chosen. This eliminates the time normally needed to look for them and train them. It also ensures all data labeling – and the challenges that come with it – are handled by one responsible entity.

Demand goes up, demand goes down, but does the price stay fixed?

Cost is intricately tied to scalability, and scalability is all about having instant access to skilled, project-certified people to meet fluctuating data demand. For data labeling, this demand is usually high at the start, but once a certain level of data annotation is reached for ML training purposes, it falls, affecting the number of annotators required.

However, if results don’t meet expectations or the project’s scope changes or evolves, the algorithms need to be retrained with additional training datasets, so the demand goes back up. The type and amount of training data needed will depend on how diverse it is (to eliminate biases) and how accurate the ML model predictions should be. In any case, the estimated costs at this stage shouldn’t exceed the budget, regardless of how much data and retraining are necessary.

Experience goes a long way… right to a complete, properly labeled dataset

Managing internal and external employees and the fluctuation in demand is an inconvenience few companies are willing to endure, especially that it involves juggling multiple contracts, worrying over annotators sitting idle, and not having full transparency into how the data is being used.

Not only is Capgemini’s data-labeling service convenient, fast, and secure, it’s also substantially cheaper and less complicated than if a company wanted to do everything alone. We have extensive experience working on many data-related projects to know exactly how to estimate the time, effort, and data required.

This gives you the freedom to plan ahead with certainty. And, since it’s our responsibility to scale the workforce up and down based on project needs, idle annotators affect our bottom line, not yours.

To learn how Capgemini’s Data Labeling Services leverages frictionless data labeling operations to deliver data at true scale, contact: vijay.bansal@capgemini.com