Improving Decision Making with Intelligent Applications

So, how do organizations empower their employees with machine learning? One option is to completely disregard the end-user experience and make the model available behind some endpoint where the user can directly query the model. This is, of course, technically complex and not entirely intuitive. For example, a user within their workflow identifies a gap in their knowledge that can be addressed with an available machine learning model, determines what information will adequately allow the model to fill that knowledge gap, and then selects the correct input to pass into the model. Next, the user exits their workflow to e.g. a command-line interface, Jupyter notebook, etc. to make a call to the model deployed model, receives a JSON output from the model endpoint, parses the model output to determine the answer to their original question, and then returns to their original workflow armed with the model output.