As artificial intelligence & machine learning are finding their way into mainstream business use-cases, the need for quality data for training AI applications is on the rise. This puts the spotlight on data annotation or the process of labeling or classifying data into various formats to enable AI/ML algorithms to identify & understand their relevance.
Businesses that use AI & machine learning to drive their digital channels with intelligent responses require tons of annotated data sets to successfully train their AI modules with algorithms to identify context & make decisions accurately. To ensure accuracy & relevance, the data MUST be annotated using well known & standard techniques & tools. Clearly, this is a serious market opportunity that several data annotation tool makers are looking to cash into.
Today most businesses need to have powerful data annotation software as well as data scientists & other AI experts to train their machine learning models. In most cases, they trust the services of a data annotation software product to create those data sets. Normally, the data annotation product company builds its product foundation on the efforts of some in-house annotators who have specialized skills in creating efficiently labeled data sets for AI & machine learning.
This often proves inadequate since these data annotation product companies often need to quickly add capacity for specific tasks. This is especially true when their customers engage them to provide a solution that comprises their product as well as a significant data annotation services component. They also frequently run into tasks that are more complex than their in-house team may have experience in. At other times, they may need to temporarily ramp up teams, knowing fully well that these teams would have to be scaled back in a short while.
It’s also not unusual for many data annotation product companies to face budget issues while addressing such challenges.
This is why they have to work with specialized data annotation services partners for specific projects or even over the long term.
So, what should these companies look for in their data annotation partners?
Here are 4 factors to consider while outsourcing data annotation background for a data annotation software product company:
Domain Experience
The beauty of AI & machine learning lies in how efficiently the software autonomously handles real operational scenarios.
For every sector, these responses & methods could be different. For example, a retail or fashion needs would be very different from Autonomous Vehicles or Sports or Security software needs.
The level of complexity of each project & each industry is different, as are the characteristics of the data to be classified. The data annotation tool you build needs to accommodate this diversity & the domain characteristic of your target sector. So, look for a partner who has experience in handling data annotation services in all your target industrial sectors to bring in the required experience that will help you build a better product.
Use their experience & expertise to make a better product much quicker.
Faster Time to Market
Getting your annotation product ready for mainstream operations involve a lot of training & experimenting. However, with the blazing pace of the market, you stand to lose market share for any delay in getting your product out. For faster rollout of a tested product, a key requirement is accelerating your training & data annotation process. An experienced data annotation services partner should have the right knowledge to enable that. But even more significantly, they must have access to a large enough team of trained people who can be readily deployed to train your tools with the right data. You will get a large collection of libraries & data sets certified for use faster, test your tool quicker & release sooner.
Of course, this applies even more when you have to deliver a data annotation services project to your customer along with your product. Your partner should be able to support your quick ramp-up, efficient delivery, 100% accuracy & sharp scale back of resources without causing you additional expenses.
Diversity of Services
Now AI & machine learning systems at your customer’s organizations work not just with text or images. Today, companies are building powerful platforms that can process media streams like video & audio to understand the context & derive insights for better business outcomes. Hence the data annotation tool they select must have the ability to label & classify data in various formats such as text, image, audio, video as well as be capable of assigning them to more diverse categories & subcategories.
There is a range of needs to address here -from the simplest to the most dynamic & complex.
To empower your data annotation tool to address even the most complex AI models & provide a better end-user experience, look for a partner who can offer a range of data annotation capabilities so you can depend on one vendor for everything.
A pro tip is to check for how the potential partner trains their people. Doing more complex data annotation calls for better-trained people with a greater range of exposure. That comes from being trained better. As your potential partner, how many hours do they typically train their new recruits?
Cost Efficiency
Budgets are tight (always). Obviously, you need a cost-effective partner to provide annotation services to control how much you spend in building your product. That will become a key input in how you cost the product too! Great services at a reasonable price could become a critical competitive advantage. A pro tip here is that the cheapest data annotation services company will likely prove to be more expensive after factoring in the productivity, accuracy & security aspects.
Data annotation products & tools face an exciting future, but they also face many challenges before they can hit the market. A great data annotation services partner can help alleviate some of those difficulties & increase the chances of product success.