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Various businesses use machine learning to manage and improve operations. While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. For example, a small data science team would have to collect, preprocess, and transform data, as well as train, validate, and (possibly) deploy a model to do a single prediction.
Netflix data scientists would follow a similar project scheme to provide personalized recommendations to the service’s audience of 100 million.
This article describes a common scenario for ML the project implementation. We will talk about the project stages, the data science team members who work on each stage, and the instruments they use.
1. Strategy: matching the problem with the solution
In the first phase of an ML project realization, company representatives mostly outline strategic goals. They assume a solution to a problem, define a scope of work, and plan the development. For example, your eCommerce store sales are lower than expected. The lack of customer behavior analysis may be one of the reasons you are lagging behind your competitors.
Several specialists oversee finding a solution.
In this case, a chief analytics officer (CAO) may suggest applying personalization techniques based on machine learning. The techniques allow for offering deals based …
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