Author: Chao Jiang | January 31st
Thousands of AI products are being developed in North American every year, in companies of all sizes. You might have an idea of the steps you need to take to develop an innovative and scalable AI product. Therefore, in this article, I want to deep dive into the one step that no one can avoid in building AI products – data annotation.
Data annotation is arguably the most important step in improving the quality of your AI algorithm. I have completed many annotation projects and managed a team of more than 200 experienced annotators since I joined Awakening Vector. In my opinion, efficiency and accuracy are two important KPIs for evaluating the quality of a data annotation project.
Whether you are considering using an external annotation service or trying to build your own annotation tool, here are my tips on how to efficiently manage your data annotation project.
Tips #1: hire skilled annotators
Data annotators often work under high pressure. They need to work intensively to complete the project on time. I remember that once my team only had 2-3 days to deliver an assignment due to the client’s project deadline. Only skilled and experienced data annotators will be able to deliver high-quality results under extreme time pressure. In addition to professional labeling skills, qualified data annotators should also have a baseline understanding of the industry where the AI product will be used. This can be done through proper training.
Tips #2: establish quality assurance (QA) standards
High accuracy is critical for your annotation project. Therefore, each company should establish a quality assurance system. For example, at Awakening Vector, we have three rounds of QA inspections completely respectively by QA testers, inspection manager and operation manager. While QA tester and inspection manager review every single data being marked, the operation manager will review random samples from the data pool.
Tips #3: effective and constant communication
It is vital to communicate across the project team, whether it’s internal or external annotators throughout the project. One thing my clients find the most valuable is the 24/7 support provided by my team at Awakening Vector. Whenever a new project is kicked-off, a dedicated project manager will be assigned to the client and will act as the point of contact between the annotators and the AI product team. Excellent communication skills and time management skills are critical for this role.
Tips #4: implement a data security protocol
Privacy and confidentiality are vital to any business. Sharing your data with annotators can be uncomfortable. There are many ways to reduce the risk of a data breach. For example, companies can sign a Non-Disclosure Agreement with the annotation service provider. Make sure that this NDA complies with GDPR. Also, all personnel and management involved in the data annotation process should sign internal confidentiality agreements as well to reduce the risk of a data breach.
To conclude, only knowing the technical aspect of data annotation does guarantee you successful training data set. Project management, communication, data privacy and most importantly, experienced annotators, are all important success factors in your annotation project. Remember, garbage in, garbage out.
About the author:
Product Manager at Awakening Vector
As a serial entrepreneur, Chao Jiang joined Awakening Vector in May 2018. He developed numerous customized solutions for artificial intelligence companies with a deep understanding of data labeling. He also helped many artificial intelligence companies improve data efficiency and reduce cost in the data labeling process.
You are welcome to contact me at firstname.lastname@example.org.