5 Data Acquisition Strategies for Supervised Machine Learning

No matter how robust an algorithm or machine learning model is, it's only ever as competent as the data used to train it. Because without data, algorithms wouldn't function, and models wouldn't be built. It's an interlinked and symbiotic process, where one aspect relies on the other to serve its greater purpose and meaning in the ML development workflow. Acquiring the data that you will feed into and power ML algorithms is the first essential step to creating, what will hopefully be, an optimally programmed model and a successful AI application that operates as it was intended once deployed. Essentially, the performance of AI systems and applications is influenced and even determined as early on as this most basic and initial effort.