Democratization of data science
To nobody's surprise, data scientists are in high demand! As a LinkedIn Workforce Report found in August 2018, there were more than 151,000 data scientist jobs going unfilled across the US (https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018). Due to this disparity in supply and demand, the notion of democratization of AI, which is enabling people who are not formally trained in math, statistics, computer science, and related quantitative fields to design, develop, and use predictive models, has become quite popular. There are arguments on both sides regarding whether an SME, a domain SME, a business executive, or a program manager can effectively work as a citizen data scientist – which I consider to be a layer of abstraction argument. For businesses to gain meaningful actionable insights in a timely manner, there is no other way than to accelerate the process of raw data to insight, and insights to action. It is quite evident to anyone who has served in the analytics trenches. This means that no citizen data scientists are left behind.
As disclaimers and caveats go, like everything else, automatic ML is not the proverbial silver bullet. However, automated methods for model selection and hyperparameter optimization bear the promise of enabling non-experts and citizen data scientists to train, test, and deploy high quality ML models. The tooling around automated ML is shaping up and hopefully, this gap will be reduced, allowing for increased participation. Now, let's review some of the myths surrounding automated ML and debunk them, MythBusters style!
