Data is the fuel for almost all AI-based companies. Some companies manage their training data needs in-house before it gets too much to handle. All the management and training efforts start to take a toll if not appropriately managed. It makes perfect sense to outsource your data annotation needs at this stage. If you're considering outsourcing your data annotation to a service provider, you've come to the right place. With our experience in building and scaling teams for training data, we'll tell you what exactly to look for when outsourcing. Then, check out the recommendations that will help you transition your in-house annotation services to a service provider.
Before you start looking for annotation partners, defining what you want to achieve with the partnership is essential. You could need someone to take the load off your team, and that's fine. Communicate it with numbers. For example, You need 3,500 frames a week. You are looking for hourly pricing that ranges from $5 to $10 You are looking for a partner who can scale up to 100 people in 3 months
Put together a document that explains the process in detail. It's essential to break down the process into steps to be able to outsource it. This instructions/guideline document will systematize a service provider's various considerations during the data annotation process.
Explain why things are done in a certain way and not in any other way. It helps people working on the task to gain a sense of purpose. The people working on the tasks should have a manual or a reference to follow when in doubt. It should be a living document that needs to evolve and be updated regularly as you find room for improvement and fix inconsistencies.
Create a dataset specifically for training purposes. Annotate it and, if possible, record the annotation process. In addition to the guidelines, these how-to videos will significantly help the annotators understand specific nuances and knacks that can't be conveyed in words.
Include examples of tasks with varying difficulty levels and complexities. Try to make it such that it covers all the do’s and don’ts of the process.
Some BPOs only provide text annotation services and some only work with transcription. It doesn't mean that they won't be able to support new tasks, but it takes time to establish quality and optimization. Therefore, it makes sense to work with a partner with specific experience in the task you want to outsource. Some examples:
Industry vertical: Autonomous vehicles, Sports analytics, NLP, Audio annotation etc. Annotation types: Bounding boxes, semantic segmentation, time-series data etc.
Engage in a timeboxed pilot period with more than one annotation service provider. Define measurable metrics for success evaluation. Always have multiple metrics, for example, speed, communication and quality of work.
Evaluate successful pilots that meet the criteria by assessing the performance across metrics to choose a service provider that suits your requirements.
Once the process is in motion, review a sample of the work conducted as a means of quality control.
Have your team who'll be using the output to evaluate the work. Try to find every single mistake and tiny things that could be improved. You might find out it's because of an unclear explanation in the guidelines that could be fixed easily. Make sure everything is communicated, and the service provider gets a clear understanding of what needs to be fixed.
One crucial thing mostly overlooked is the evaluation of the process itself. It doesn't make sense to what your service provider does as long as you get the output when you outsource - true. Still, you get to understand how they bring operational efficiency to the process, and you might have some of your suggestions in improving the quality of the output or the speed.
Keep an open exchange of communication with the service provider. Questions, feedback and suggestions need to flow both ways to gain a clear understanding of the task at hand and achieving it with as few mistakes as possible.
Create a document of all the questions and clarifications to become a knowledge base for you as well when you evaluate other service providers.
These recommendations will help you evaluate an annotation service provider, but there are by no means complete. Use your own experience and your requirements to choose an annotation partner. If you're in the process of evaluating annotation partners, do get in touch with us.
Here's what the founder of an autonomous robot startup had to say about our services,
"We have previously used a number of US based services for our image annotation tasks but none were able to provide the personalized attention to detail like DataClap did."
Our data annotation efforts helped our client implement a smart city project with challenging requirements.
We helped a sports analytics leader in developing AI models for sports analytics.
Case study on how we provided image annotation services for an autonomous robotics startup
Case study on how we used Human in the Loop for data curation
Case study on how we used Human in the Loop for a document processing use case
Check how we helped a client in increasing the performance of their models.
Contact us with your requirements and we will set up a team to wok on your free pilot project. No commitments on your side.
