1. Predictive Annotation Tools
As data annotation requirements grow to meet the rapidly expanding needs of the Artificial Intelligence and Machine Learning market, a key capability that will come into further focus is the use of predictive annotation tools i.e tools that can automatically detect and label items based on similar manual annotations. In a Computer Vision workflow, for example, a tool that can annotate subsequent frames after the first few are manually marked, is a valuable addition to the toolkit. Human intervention may still be required in the form of minor editing, but the overall time and effort saved has a huge impact on throughput. The development of predictive tooling with sophisticated features will be a key focus in the data annotation ecosystem.
2. Customized Reporting
The annotation process consists of several workflows and processes, especially while working with large expert annotation teams. Detailed reporting, particularly on quality and throughput, is crucial to analyze the workflow’s productivity, and make informed decisions about project progress. The use of APIs and open source tools will enable complete report customization, with the use of drag and drop filters. Reports with details down to the individual annotation level will become part of the reporting suite. Staff activity is monitored through real-time reporting and resource allocation can be tackled dynamically to handle workload fluctuation. Value is also added throughout the annotation process through pattern spotting and trend analysis over time, which enables cost saving.
3. Increased Focus on Quality Control
In the future, there will be an increased focus on quality control data engagements at scale. As more data labeling solutions go into production, and thereafter into the training of the model, more edge cases will be identified and focused on, during the process of quality control. Teams focused exclusively on quality control will be built, comprising experts who have a deep understanding of the data and the subject matter contained in it. These specialized teams will be able to function without detailed guidelines and be hyper-focused on spotting and fixing issues within large-scale datasets
4. Workforce of SMEs
As more industries embrace the use of AI, the need for subject-specific data annotation teams will rise. Teams trained with custom curricula will be deployed on projects within specialist domains like healthcare, finance, and the public sector, building expert workforces over time. The focused but deep approach of the expert data labeler adds value to the annotation process, right from the validation of guidelines to the time of data delivery.
5. Specialized Partner Ecosystem
A robust partner ecosystem has been in the making within the data annotation domain and will continue to be essential. The ability to quickly navigate a big ecosystem for specialized capabilities will be mission-critical for the deployment of AI. With each provider within the ecosystem bringing specialized expertise either in data labeling or metadata creation or in the setup of flexible and productive workflows, less time is spent in solving problems that have already been solved. When a customer works with a company within an ecosystem, recommendations can be made for the best combinations of specialized tools and customized capabilities for that specific project and workflow.
