There are some ready-made human in the loop machine learning tools that allow you to label training datasets and verify the outcome. However, you might not be able to implement the tips above with these standardized tools. Here are a few human in the loop tool examples:
Google Cloud HITL
This solution offers a workflow and a user interface (UI) that people can utilize to label, review, and edit the data extracted from documents. The client company can either use their own employees as labelers or can hire Google HITL workforce to accomplish the task.
The tool has certain UI features to streamline labelers’ workflow and filter the output based on the confidence threshold. It also allows companies to manage their labelers' pool.
Amazon Augmented AI (Amazon A2I)
This human in the loop artificial intelligence tool allows people to review low-confidence and random ML predictions. Unlike Google Cloud HITL, which only operates on text, Amazon A2I can complement Amazon Recognition to extract images and validate results. It can also help review tabular data.
If a client is not happy with the supplied A2I workflow, they can develop their own approach with SageMaker or a similar tool.
DataRobot Humble AI
Humble AI permits people to specify a set of rules that ML models have to apply while making predictions. Every rule includes a condition and a corresponding action. Currently, there are three actions: