I was inspired to share my experiences with automation and AI especially experiments with edgeTI edgeCore™ platform. edgeCore is used by large enterprises, service providers, and governments to understand their business and technical operations and then take action with speed and intelligence. While my story started some 20 years ago, the connection to the present proved to be enlightening and assuring that EedgeCore turns out to be a great way to instrument AI and its impact on operations.
I spent over a decade writing self-tuning, tier-1 and 2, threshold-based solutions designed to interact with large amounts of remotely-located, unattended or under-attended machines. Under-attended machines are those machines that do have an operator, but that operator doesn’t have a good grasp of the functions being performed for them by their hardware and software.
I came to edgeTI in November of 2018. When I arrived, Edge was already bringing API-based and web-based data and presentations through its platform. The platform allowed the user to create actions that would present to the user when certain conditions were met. Our most advanced customers had learned how to take those threshold-based actions and implement simple automation.
With edgeRPA customers could orchestrate activity through our platform that would pass-through Citrix, browser, and directly within Windows. This feature allowed our clients the unprecedented ability to act upon data moving through a remote pipeline to prompt individual operators and to guide those operators through automated steps. The result was dramatic time-savings, less rework, and fewer errors. At this point, we were still threshold-based. The adjustment of thresholds was updated automatically in some places but was otherwise largely manual.
In mid-2019, we launched a feature that would shift the way our customers used their data. As data moved through our pipeline, on its way to the operator, we could take pieces of that data and process it against external APIs. We used this function to allow our customers to take operator-bound data and begin to train AI models. Our proof-of-concept used an AWS SageMaker model. As rows of data progressed through our pipeline, we would take that opportunity to place observations into the model designed to handle that data.
At first, the observations just accumulated. But once the model was trained and the acceptable confidence-level was established, we shifted the operators’ view.
Where once there was a noisy bunch of data flowing through a data-pipeline, now it was attached to the SageMaker model data source. The data the operators now saw, streamlined to solely highlight the established anomalies. Multi-page datasets could be reduced to just a few lines, and ordered by like severity.
The actions edgeCore had already allowed could now be executed with higher confidence by the operator. But we didn’t stop there.
With streamlined datasets, we undertook the next step of the AI puzzle. We could record the observed anomaly and the follow-up action that the operator used to resolve. This combination of keys was stored in a new model. The new model, once trained, would allow us to re-order the actions offered based upon the historical observation based on the operator-user-base. Eventually, we would automatically be able to take high-confidence actions and report those actions to the operator. Finally, the actions would be taken, recorded, and the anomaly would never appear to the operator.
The old adage, automating a mistake just allows you to make them faster, is fair. The steps to automation, with a progressive plan-of-action and the correct tools, can be taken. It’s a tractable problem, with a lot of solutions. The key is don’t automate what you don’t understand. These are the steps to a successful AI solution: