Training as a part of the workflow

CMR+ offers the capability to incorporate training as part of the workflow, allowing the machine learning (ML) models to continuously learn and improve as they process more and more documents.

The training component of CMR+ enables you to provide feedback and corrections to the ML models based on the results of document processing. This feedback loop helps the models identify and correct any errors or inaccuracies in the extracted data, classifications, or predictions. By incorporating training into the workflow, the ML models can learn from these corrections and adjust their algorithms accordingly, leading to improved accuracy and performance over time.

The process of training the ML models within CMR+ is typically straightforward and user-friendly. When discrepancies or errors are identified and flagged during document processing, citizen developers can provide the correct information or annotations. The platform then leverages this feedback to refine the underlying ML models, updating their knowledge and enhancing their ability to accurately process similar documents in the future.

Additionally, CMR+ employs advanced techniques such as active learning, which optimizes the training process by intelligently selecting specific documents for human review. By focusing training efforts on the most challenging or uncertain cases, the ML models can learn more effectively and efficiently, saving time and resources while improving performance.

Furthermore, the training component can be integrated into an iterative workflow, where the ML models are continuously retrained as new labeled data becomes available. This ongoing learning approach ensures that the models stay up-to-date with evolving document patterns, industry-specific terminology, or changes in document formats. As a result, the performance and accuracy of the models steadily improve with each iteration.

The training-as-part-of-workflow capability in CMR+ offers several advantages. It enables the ML models to adapt and learn from real-world scenarios, allowing for continuous improvement and refinement. This iterative learning process ultimately enhances the accuracy, reliability, and efficiency of the document processing workflows, ensuring that the models align closely with the specific requirements and nuances of your organization.