Alegion, a training data platform for artificial intelligence and machine learning initiatives, announced the release of its next-generation platform with new features designed to boost large-scale machine learning initiatives and model confidence for enterprise AI systems.
The platform integrates trained data specialists with data task management and distribution capabilities to accelerate machine learning projects through the creation of training data, model testing, and exception handling at scale for sectors such as financial services and government, for examples.
New capabilities are:
- Machine learning using predictive indicators to score judgments per task and dynamically determine appropriate additional quality control stages like consensus judgments, review/adjudicate/exception workflows and administrative reviews.
- Human intelligence sourcing, including the ability to supply private or specialized workforces and create hybrid workforces fro Alegion’s own data specialists and partners, while maintaining the benefits of its quality controls and task management capabilities.
- Programmatic integration of human intelligence and AI, which accelerates model training and testing by taking data as it is generated, processing it, and returning it to the model in real-time.
The intent is to reduce the amount of time data scientists spend on managing data quality issues, estimated at some 80%, as well as project risk, since nearly half of AI projects fail due to data problems, according to Nathaniel Gates, Alegion’s co-founder and CEO.