The goal is to get machine learning techniques into the hands of industrialists. The challenge is the complexity involved in accurately training machine learning algorithms. This typically requires data science expertise, but as more generalists become more tech savvy, the power of machine learning can be put into the hands of folks closer to the data. In the IIOT world (Industrial Internet of Things), Operations Engineers are often the most familiar with the data coming off the sensors of industrial equipment.​​​​​​​
Product Strategy
As Principal Product Designer at GE Digital, I worked closely with stakeholders (customers, product managers, and business leaders) to help define product roadmaps. I worked closely with international scrum teams (California, Bangalore, New York) to ensure consistent user experience, and ensured that the conceptual models and tools were intuitive to users and provided customer value.
Storyboarding and concept flows help stakeholders, product managers, and engineering teams quickly grasp the high-level user flow of the product.
Contextual Observation
To better understand users, I worked closely with a UX Researcher to visit customer sites to observe user workflows and gain critical insights into user behaviors. Industry roles for this line of products include Reliability Engineers, Data Analysts, and Data Scientists.
Data Visualization
During interviews with subject matter experts, it becomes obvious that data exploration and data visualization is a critical part of the process. Data visualization tools let users evaluate data before and after analytic processing.
Equipment Dashboards
The data output of the deployed analytic models feed into prognostic dashboards, giving users insights to make better on-the-job decisions. 
The primary personas for these dashboards are Reliability Engineers and Performance Analysts. The visual look for the equipment monitoring dashboards is dark because industrial equipment monitoring facilities are often in lowlight environments.
The data science process is very iterative, and often never complete because machine learning models might get “stale” due to "data drift". I led the team in designing monitoring tools so that Operators can ensure that the predicted results are close to actual results. If not, the machine learning model may have to be retrained.
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