Many businesses and organizations are turning to machine learning for solutions to challenging business goals and problems. Providing machine learning solutions to meet these needs requires that one follows a systematic process from problem to solution. The stages of a machine learning project constitute the machine learning pipeline. The machine learning pipeline is a systematic progression of a machine learning task from data to intelligence. During our training as ML engineers, a lot of focus is invested in learning about algorithms, techniques, and machine learning tools but often, less attention is given to how to approach industry and business problems from the problem to a usable solution.
AI and machine learning are going to start making a lot more decisions. They probably still won't be used in the near future to make "big" decisions like whether to put a 25 percent tariff on a commodity and start a trade war with a partner. However, nearly anything you've stuck in Excel and massaged, coded, or sorted is a good clustering, classification, or learning-to-rank problem. Anything that is a set of values that can be predicted is a good machine learning problem. Anything that is a pattern or shape or object that you just go through and "look for" is a good deep learning problem.
Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations. It's specialized work, calling for strong business acumen, some technical knowledge, and project management and delivery chops. Deploying translators is especially important during a company's early efforts to use analytics, when much of its analytics know-how resides in a small cohort of data leaders and practitioners. We've seen companies hatch ambitious plans to apply analytics in dozens of situations--only to pull back because they employ too few people who can deliver solutions.