While there are several benefits to data labeling outsourcing, there are times when in-house data labeling makes more sense than outsourcing. You can choose in-house data annotation when:
Expert Data annotators
Let’s start with the obvious. Data annotators are trained professionals who have the right domain expertise required to do the job. While data annotation could be one of the tasks for your internal talent pool, this is the only specialized job for data annotators. This makes a huge difference as annotators would know what annotation method works best for specific data types, best ways to annotate bulk data, clean unstructured data, prepare new sources for diverse dataset types, and more.
With so many sensitive factors involved, data annotators or your data vendors would ensure that the final data you receive is impeccable and that it can be directly fed into your AI model for training purposes.
Scalability
When you’re developing an AI model, you’re always in a state of uncertainty. You never know when you might need more volumes of data or when you need to pause training data preparation for a while. Scalability is key in ensuring your AI development process happens smoothly and this seamlessness cannot be achieved just with your in-house professionals.
It’s only the professional data annotators who can keep up with dynamic demands and consistently deliver required volumes of datasets. At this point, you should also remember that delivering datasets is not the key but delivering machine-feedable datasets is.
Eliminate Internal Bias
An organization is caught up in a tunnel vision if you think about it. Bound by protocols, processes, workflows, methodologies, ideologies, work culture, and more, every single employee or a team member could have more or less an overlapping belief. And when such unanimous forces work on annotating data, there is definitely a chance of bias creeping in.
And no bias has ever brought in good news to any AI developer anywhere. The introduction of bias means your machine learning models are inclined towards specific beliefs and not delivering objectively analyzed results like it’s supposed to. Bias could fetch you a bad reputation for your business. That’s why you need a pair of fresh eyes to have a constant lookout for sensitive subjects like these and keep identifying and eliminating bias from systems.
Since training datasets are one of the earliest sources bias could creep into, it’s ideal to let data annotators work on mitigating bias and delivering objective and diverse data.
Superior quality datasets
Like you know, AI doesn’t have the ability to assess training datasets and tell us they’re of poor quality. They just learn from whatever they are fed. That’s why when you feed poor quality data, they churn out irrelevant or bad results.
When you have internal sources to generate datasets, chances are highly likely that you might be compiling datasets that are irrelevant, incorrect, or incomplete. Your internal data touchpoints are evolving aspects and basing training data preparation on such entities could only make your AI model weak.
Also, when it comes to annotated data, your team members might not be precisely annotating what they’re supposed to. Wrong color codes, extended bounding boxes, and more could lead to machines assuming and learning new things that were completely unintentional.
That’s where data annotators excel at. They are great at doing this challenging and time-consuming task. They can spot incorrect annotations and know how to get SMEs involved in annotating crucial data. This is why you always get the best quality datasets from data vendors.
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