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Most Machine Learning projects that are deployed to production, fail which is a scary thought when you consider the TCO. Heavy investment goes into dedicated staff members, compute resources and research, however if there is no ROI, what value can be derived from the project?
This magazine was produced to educate stakeholders in Data Science, Data Mining and Data Engineering along with highlighting the importance of communication and collaboration with the Data Science and Data Analytics teams. Everything starts with the business goal and if this isn’t articulated to the Data Science team, it is highly likely that the end result will be unsatisfactory.
Data Scientists are extremely talented people but without proper management and guidance they have the potential to get lost in the Data. It isn’t necessarily the model that you design, it is the model a Data Engineer can build so there must be cohesive collaboration with all teams that are unified in solving a problem and providing a business outcome. AUC (Accuracy Under the Curve) denotes the precision and accuracy of a model but if that model accuracy hasn’t been validated using training sets and hold out data, then the results achieved, could potentially be null and void.
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