There exist several data annotation companies to choose from, each with their own operational workflow, quantitative evaluation, training methodologies and tools. It is the combination of these factors that determine the quality of your training datasets and ultimately the performance of your algorithm.
At Takt, we have developed unique processes based on years of experience dealing with datasets of varying complexities and need for scalability with affordable pricing. We know exactly how to achieve a dataset-annotator fit having studied the psychology behind the annotation process. In order to ensure that you make an informed decision in selecting the data annotation company, we have condensed our expertise into 23 questions across 5 distinct categories each of which represent key areas -
1. Methodology for Quality
Aim: Understand how they derive quality through uncertainty
2. Pricing and Alignment of Incentives
Aim: Ensure the annotators are motivated for quality and you get the maximum per dollar spent
Aim: Confirm you can count on their quality and delivery when your volume increases
4. Data Security
Aim: Guarantee your Data’s Privacy
5. Technology and Tools
Aim: Learn about quality and efficiency metrics, proprietary in-house tools and APIs and real-time annotation
Your machine learning algorithms are driven by the quality of the data they train on. We know that small differences in the quality can make or break your algorithms. We also know that training an algorithm is not a one time task. It is a continuous process where you test and validate your models and enrich your corpus based on the results. A data annotation team needs to be able to adapt to your requirements, keeping quality as the priority.
The following questions will help you understand their importance placed on quality -
We know the harsh truth: Every annotation task has a maximum budget. Every annotation company can work within that budget, however, at the cost of quality. It is imperative that pricing in a company incentivizes annotators for quality rather than volume of output. It is also important that as a client you gain economies of scale as your volume requirements increase.
The following questions verify these requirements -