The five stages of machine learning implementation: Page 3 of 3

January 09, 2017 // By Alexander Khaytin
The five stages of machine learning implementation
Imagine your company was planning to transition into Industry 4.0. Now imagine that it’s your job to implement the big data analytics, machine learning and artificial intelligence technologies needed, into the business environment. You’re going to need to know: where to begin, what kind of problems to expect, and how the specific related projects and services differ from what you are used to.

Stage 4 – Integration and testing

After model training, it is integrated into the client’s management system. This integration is simplified by the fact that the model realises only one function – it predicts or recommends. In this regard, the interface is very simple and the integration is reduced to data transfer and displaying those recommendations.

Practical tests are carried out with client’s side experts participating. During testing, it is necessary to measure the accuracy of the model, and achieved economic effect. In the service designed for MMK, the board of experts involved in the testing waived any responsibility from the operator if he did not quite agree with the recommendation made. This allowed us to estimate the effect of the model use and to determine the necessary changes for its use in production. During testing, model showed a decrease in ferroalloy use of about 5%, and the projected savings comprised $4.3 million per year.


Stage 5 – Model monitoring

The last stage is the production use of the service that successfully passed testing. The production use requires constant monitoring of the model quality and its regular additional training on newly collected data. Unfortunately, completely self-trained machine learning systems are in the research stage now, and their application for business and industry use is not possible.


About the author:

Alexander Khaytin is Chief Operating Officer of Yandex Data Factory -

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