Training data is a crucial component in the development of AI and machine learning models, and high-quality computer vision datasets are a primary requirement.
Poor in-house tooling, labeling rework, difficulty locating data, and difficulties in collaborating and iterating on distributed teams’ data can lead to problems.
Workflow alterations, large datasets, and an ineffective data training workflow can hinder an organization’s growth, especially if it’s growing too rapidly. An example of such an industry is the highly competitive autonomous vehicle industry.
Scalable training-data strategies are essential in this industry, and a company that cannot adapt may suffer from customer dissatisfaction and financial loss.
A company’s training data strategy may need to adapt quickly for various reasons, such as generating a high volume of raw data that needs to be labeled or developing a solution that requires a significant amount of real-time data.
Finding the optimal data annotation strategy may come late in the development process, leading to a waste of time and money. Data annotation feedback loops and agile methodologies are crucial for success.
Companies can either hire an internal team of annotators, work with freelance annotators, or rely on a data annotation platform.
Creating an in-house data annotation team brings the benefits of process control and QA, but it also carries additional costs and risks, such as HR resources, management of a new team, and software development to support data annotation and workflows.
This method is not scalable, and teams that try to build in-house tech solutions often lose strategic development time instead of outsourcing the data annotation process. Third-party data annotation tools are usually more sophisticated and come with experienced annotators and skilled project managers.
Outsourcing data processing tasks to an industry expert is a cost-effective solution for small-size and temporary projects. However, this approach has limitations, such as the lack of expertise among outsourced labelers, leading to poor training data quality.
Companies that have built and sold their own data platform provide self-service platforms that enable companies to efficiently self-manage their annotation projects with advanced capabilities, robust UI, advanced annotation tools, and ML-assisted annotation features.
In conclusion, choosing the right data annotation strategy is essential for the success of an AI or machine learning model. Building an in-house team may not be scalable, while outsourcing may lead to reduced data set quality, consistency, and confidentiality.
Companies that rely on third-party data annotation tools or self-service platforms may benefit from advanced capabilities, experienced annotators, and skilled project managers.
