Deploying machine learning models which predict an outcome across a business is no easy feat. That’s particularly true given that data science is an industry in which hype and promise are prevalent and specifically machine learning — although a massive competitive differentiator if harnessed the right way — is still elusive to most brands. There is a multitude of potential hurdles and gaps standing in the way of actuating models into production, such as skills gaps (both internally and with vendors or providers) and the possibility that your data or the models themselves don’t possess enough integrity and viability to produce meaningful results. …

In this post, we’re going to do just that…unpack the various elements of setting up and deploying a machine learning model which effectively predicts what’s needed to drive successful business outcomes. Read More

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