How to make the most out of your data labeling solution

Data labeling – a fundamental cornerstone of artificial intelligence (AI) and machine learning (ML) – is a tedious, time-consuming, and ultimately crucial component of any AI or ML investment. Training datasets require high quantities of accurately labeled data, the preparation of which can easily take up more than 80% of time spent on ML projects.

The process, should a company opt to handle it on its own, takes up a lot of company resources, investment, and training. Fortunately, there are other solutions available to the AI and ML-driven company, each with their pros and cons.

What are these solutions?

  1. Crowdsourcing

This solution quickly and cheaply delegates data labeling tasks, at scale, to a large and diverse contributor base. It conveniently goes past traditional talent sourcing barriers, providing access to thousands of sources with relatively little effort upfront. Many overhead costs are eliminated, as contributors are paid per task completed.

However, the workers are also unmanaged, which may hamper quality, compliance, and visibility into processes. They work untethered from company stakeholders, limiting a company’s ability to customize work to new challenges. Their freedom also means little long-term value is created; their value as a workforce is purely temporary.

  1. In-House Teams

Here, the company handles the entire data labeling process internally. An upside of this is that teams will be able to achieve subject-matter expertise specific to the company’s core processes and objectives. Using such teams, or even creating in-house labeling tools, also creates long-term value for the organization, should its requirements remain the same.

That said, recruiting, training and optimizing such teams require significant amounts of time that could be used otherwise for innovation. Getting all the pieces in place and keeping things going is a heavy investment, both in time and money.

  1. Fully Managed Third-Party Solutions.

Such solutions are what organisations like Supahands provide. End-to-end service partners like us are able to understand your project needs, recruit the right workforce, and supply the best-in-class labeling tools. The workforce is fully managed, meaning that we can customize and adapt our service to whatever evolving requirements you may have. And no matter how big or complex your data is, a good data labeling partner will meet quality, accuracy, and any regulatory requirements. 

One must be careful in hiring such a partner, of course. Ending up with the wrong partner can be costly. Even the right partner may lack flexibility, often requiring clients to commit to a certain volume of data or an upfront fee.

Weighing the solutions

How do you decide, then, which approach is best for you? Different circumstances and requirements call for different solutions. There are various factors one must consider. There’s the matter of the project’s timeframe – how fast does it need to get up and running? – and budget – how much money do you have at hand for an upfront investment, and how much can you pour in long-term for scaling and support? AI and ML research group Cognilytica reports that for every “1x dollar spent on third-party data labeling, 5x dollars are spent on internal data labeling efforts.

There’s also the question of scalability. How quickly will you need to expand your capacity for new projects as your business evolves? Your business will also need to maintain the process’s quality, in terms of the specialized training and quality assurance such data annotation will require. 

Diversity is another factor; depending on the data, you’ll need to estimate your workforce’s capacity to limit risk and produce unbiased results. Finally, there’s the question of management. Do you have the internal bandwidth, staff, and experience to manage a data labeling workforce, should you decide to handle it in-house?

Choosing the right solution is crucial. Opt for a solution that guarantees and delivers on accuracy, as well as speed, scalability, and cost savings. Your long-term satisfaction, and your ability to reduce business risk as you settle on a winning solution, depend on these factors.

For a better understanding of the solutions available to an AI and ML-driven business, download our latest e-book and learn more about their pros and cons, as well as the various factors that go into picking the best one for you.

Want to learn more about how to make the most out of your data labeling solution?

Supahands, an end-to-end data labeling partner

Here at Supahands, we help AI, ML, and computer vision-driven companies prepare their training data with a fully-managed team and end-to-end service. We ensure effective communication, agility and adaptability to edge cases.

New to data labeling?  Not to worry, we ask the necessary questions for new projects, to help you along on your journey. Data labeling is our thing, so we can offer you a: 

• A deep understanding of your project and business needs

• The right workforce and business project managers for your projects

• A pool of 13,000 highly trained SupaAgents provided to scale

• Our SupaAnnotator platform, which ensures high accuracy, compliance, and success

• The most flexible and customized pricing and package options in the market

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