Annotating image and video data for machine learning is a substantial challenge for all AI companies, but for startups it can be a significant bar to development. Accurate, quality image annotation requires a large investment of time and resources, potentially detracting from the core mission of companies at a precarious time in their development.
Startups are often defined by pressure. Failure to produce a working product, within the strict time limits set by investors, will likely result in the failure of the company. Setting up an in-house data annotation team can add to this pressure, hampering efforts to produce a successful computer based machine learning model.
Outsourcing to professional annotation services can help to alleviate some of the stresses associated with starting a machine learning company. This blog will address four challenges that image and video annotation presents to startups and suggest ways in which outsourcing to managed teams of experienced annotators can help.
The burden of management
Managing an image or video annotation process represents a significant investment of time for company leadership and senior staff. In smaller organisations it is essential that these individuals are focused on the core mission.
Organising and managing large teams of annotators, often working remotely in distributed locations, can be a real distraction. The responsibility of ensuring quality also falls to these key individuals, as does troubleshooting.
Professional annotation services, like Keymakr, are experienced at managing teams of annotators. Quality issues and troubleshooting can also be communicated to annotation providers, freeing up the core startup team to meet their development goals.
Scaling up and down
The demand for data in a machine learning startup often varies significantly. A small in-house team may have too much work at some periods and be underused at others. And as the company grows changes in demand will become increasingly common. Annotation operations will have to be ramped up, and this can mean significant investment in hiring and management.
Outsourcing to experienced providers allows startups to adjust their data demands as the needs of their machine learning models change and grow. Flexibility is one of the key advantages of looking outside the organisation for help with annotation.
Training teams of annotators is also a time consuming task for startups, that requires a substantial amount of expertise. Annotators need to be trained up on how to use annotation tools and how to achieve the results required by the project. Constructing and administering a training program can take time away from productive research and development.
Annotation services already employ their own teams of experienced annotators. Some providers, such as Keymakr, have managed teams in one location. This ensures that management and training is always the highest priority.
The cost of annotation
Hiring annotators, training them, and purchasing annotation tools can be a significant financial burden for companies already operating with constrained resources. Providing office space, and administrative costs for teams of annotators represents a large early investment for fledgling startups.
Outsourcing can provide an affordable solution for startups. Competitively priced annotation services can ensure that computer vision projects receive the quality data they need without hampering their business with excessive data creation costs.
Outsourcing can support startups
Keymakr utilises bespoke annotation tools, managed teams of annotators, and multiple layers of quality control to provide data annotation that is precise, affordable and scalable.