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The style of how data science solutions are delivered varies a lot. This depends on the objective of each project. Here’s a threefold classification scheme that I encountered most of the time:PowerPoint and Excel: Insights from the are simply presented in some slide decks as quick wins. This occurs mostly in the first stage of a project, after the initial data analysis. Examples are word clouds or simple descriptive charts about the most important features.Self-Service Tools: Customers are now able to perform the analysis on a high-level abstraction scheme, e.g., through some prototypes that give them a look and feel. Before that, the data was dumped to the data scientist. The consumer is now able to gather key insights about potential functionalities of the product. A very good tool is R Shiny that allows you to easily build great UIs.Real-Time Production: After passing the two stages mentioned above, the solution needs to be implemented in the productive systems. This is the most challenging task. In the stages before, everything happened in a sandbox. The data scientists had their dataset and own technical infrastructure. Now they are dealing with new questions like scalability (is the algorithm quick enough to make real-time decisions), data quality (how do we handle inferior quality) and of course, performance (was the model trained on that small sample representative). Those are the issues that prevent most projects from being used in the real world. However, all aspects were addressed, the solution is integrated into working systems.

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This is a great question, and a perfect one for any data scientist/ML engineer to consider.At wise.io (a part of GE Digital), we build templatizeable [1], ML-powered applications for the industrial internet—the catalogue of connected sensors, industrial machines, and assets that make up the complex systems in spheres like power, aviation, oil and gas, and healthcare. Personally, this means two things: (1) creating and scaling new ML applications for use in internal GE businesses and by GE customers, and (2) increasing the power, breadth, and robustness of our standard templates to better support these new applications, with the eye towards making them even more capable of powering the next round of applications. This split mirrors the ethos of the company—there is value for sure to be gained in each application we build, but the real long-term gains are won with the ability to rapidly prototype and productionize those applications across a host of use-cases and related products. So these are the twin business problems I work on day-to-day: empowering industrial stakeholders through production-grade ML tools, and building out the capability to leverage ML in the industrial sphere through readily-deployed ML.It’s a big mission. GE-built assets account for something like half of all commercial aircraft engines in existence, 30% of all power generated worldwide, and a plurality of medical imaging devices; and that’s just 3 of 7+ major business verticals inside the company. It’s a lot of data, and with an ML ecosystem that is somewhat less developed than the consumer internet (with the Facebooks and Googles having infused said space with ML-powered apps), there’s a lot of opportunity to do some really cool and novel things. If the challenge of that sounds exciting, we’re hiring enthusiastic and capable contributors across the data spectrum: from backend ML engineers, systems integration engineers, DevOps, and of course data scientists to work at our downtown San Francisco office—feel free to hit me up on LinkedIn if you think you’d be a good fit :)[1] This is a buzzword that isn’t meaningful without exposition. In this case, templatizeable means Dockerized processes built with re-useable parts, with common tools around testing, monitoring, and validation of models, along with scoped and defined processes for model training and deployment, all of which exhaustively tested.