When Should You Scale Your Data Labeling?

data labelling
data labelling

The “AI in Short” series is a collection of shorter pieces that supplement my longer articles and provide bite-sized and readily usable information about AI in a modern business. Watch out for more coming soon.

In the age of data-driven decision-making, the need for data labeling has never been greater. Data labeling is an essential part of training, testing, and validating machine learning models. But with the ever-increasing demand for labeled data, business leaders are often faced with the question of “when is it time to scale?” After all, data labeling can be time-consuming and requires careful iteration. Luckily there are a few tell-tale signs that you should consider when deciding if it’s time to scale your workforce or outsource your data labeling needs.

Sign #1: You’re Spending Too Much Time Wrangling Data

Data wrangling is an essential part of any machine learning project. But if your team is spending too much time manipulating and cleaning raw data before they can even start to label it, then it may be a sign that you need to increase capacity. This could mean bringing in extra resources or outsourcing some or all of your data labeling needs. Outsourcing can help reduce costs by allowing you to pay only for what you need when you need it.

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