The five stages of machine learning implementation

If you’ve stumbled upon this article, you may already be in this position. However, what’s more likely is that this is going to become your situation in near future, and learning from someone else’s experience is now needed to prepare. While there’s a plethora of theory around business applications for data analytics; there is a significant lack of practical, real-life experience to draw on. This is largely due to the fact that adoption of these technologies, for many industries, is new and the results of pilots are just coming to light now. Drawing on our work with one of the world’s largest steel producers, here I will detail some of our most useful and practical learnings.

Machine learning technologies are successfully used in predictive and recommendation services. The basis of accurate predictions is formed by historical data which is used as a training set. The result of this work is one or more models that can predict the most likely outcome of the technical process or the set of options, among which the best is chosen.

For example, Yandex Data Factory developed a recommender service for Magnitogorsk Iron and Steel Works (MMK) that helps to reduce ferroalloy use by an average of 5% at the oxygen-converter stage of steel production. Not only it saves about 5% of ferroalloys but, more importantly, this happens with sure and steady maintenance of the high quality of resultant steel.