Developing an AI model is expensive, right? For a lot of companies, the mere idea of developing a simple AI model could push them to assume they would need millions of dollars to develop it. Oftentimes, they turn out to be true as well. However, every cost that you incur should give you significant returns. That’s the only way you know you’ve invested in something wisely.
But there are a few expenses managers or business owners incur due to their negligence, miscalculations, or poor decision making. One such major mistake managers make is deciding whether to prefer internal data resources and team members to annotate their datasets or outsource the entire process.
While this idea stems from the intention to save on expenses involved in outsourcing data annotation projects, they often overlook several factors and touch points that ultimately make them spend more in the long run. A lot of stakeholders are under the misconception that preferring internal data annotation modules will help them save on expenses and complete AI development projects on a decent budget. However, that’s where expenses start cropping up.
Such decisions compel managers to incur losses due to several reasons including lack of adequate datasets or data generation touch points, absence of relevant data, an abundance of unstructured and uncleaned data, overhead expenses to train team members to annotate data, rent or purchase annotation software, and more.
In the long run, they end up spending twice or more than what they would spend on outsourcing the entire project. So, if you’re someone still in a dilemma whether you should go for data annotation vendors or assemble an in-house team, here are some eye-opening insights.
4 Reasons You Need To Outsource Your Data Annotation Projects
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.
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 touch-points 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.
Apart from these factors, the major advantage you will have when you outsource data annotation to vendors and experts is time. AI development is complex and you will have diverse tasks and requirements to work on. Data annotation is another added responsibility for your team members. When you outsource, you can let them spend more time on tasks that actually matter for your business and project.
In short, outsourcing your data annotation project could help you increase your internal productivity, have a faster time to market, offer you more time to test your results and optimize algorithms, and more. If you’re looking to save more time, simply reach out to us for all your data annotation needs.
Our ensemble team involves SMEs, veteran project managers, data scientists, and more who work on delivering the finest quality datasets for your AI project. Talk to us now.
Originally published at https://www.shaip.com.