When Should You Scale Your Data Labeling?
AI in Short — The Human Component of the AI Stack
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.
Sign #2: The Volume Of Data Labeling Is Increasing Rapidly
As the demand for labeled data increases, so too does the amount of time needed to complete those tasks. If your organization has seen a sudden increase in the volume of labeled data needed, then it might be time to scale up your workforce or consider outside services as an option. If done correctly, outsourcing can help reduce costs while also freeing up valuable resources within your organization that could be used elsewhere.
Sign #3: You Need A Variety Of Data Types And Formats
In order to train machine learning models correctly and accurately, you will need access to a variety of different types and formats of labeled data. This can range from text documents and images to audio files and videos — all needing to be labeled appropriately before being used in training models. If you find yourself struggling with this task due to a lack of resources or expertise, then scaling up your workforce or outsourcing may be the best option for meeting these needs in a timely manner.
Maximizing the Time of Data Scientists and ML Engineers
Data scientists and machine learning engineers face mounting pressure to maximize the use of their time. With expensive salaries, it’s essential to ensure their efforts are directed in a way that provides value to the organization. Data labeling can draw attention away from more innovative endeavors, resulting in significant outflows of precious resources.
Therefore, instead of solely focusing on data labeling, businesses should enable their experts to spend time on more analytical projects with higher returns — elasticity is also necessary to adjust staffing levels as needed. Ultimately, costs can be contained while still providing access to data scientists and machine learning engineers when businesses have sudden spikes in data input.
Succeeding With High-Quality Data
In today’s world, having access to high-quality labeled datasets is key for success when working with machine learning models and other types of artificial intelligence technology. As such, business leaders must take into consideration whether or not their organization has adequate capacity for meeting these demands in-house before making any decisions regarding scaling up their workforce or outsourcing their needs altogether.
By keeping an eye out for certain tell-tale signs, such as increased wrangling times and rising demand for labeled datasets, executives can ensure they make informed decisions about how best to tackle their organization’s data labeling needs going forward!
