AI News, Practical data science: Building Minimum Viable Models

Practical data science: Building Minimum Viable Models

When we talk about innovative services or products, many startups follow a smoother model of development.

For those startups based on data (nowadays, most of them consider their data as a strategic active for the decision making), to find a model that interprets them is a difficult task.

It is the data scientist or the data science team’s task to find that/ those model/s, but finding it/them (determine the modelling technique, setting parameters and adjustment) may be a very long, and sometimes, non-aligned task with the business times.

This working methodology based on minimum and effective models, minimizes the risks in the event the product does not succeed in the market and, therefore, is an obstacle less in regards to the launching.

It is not necessary to develop a prediction algorithm 100% effective and it is not feasible in financial terms. Every sector/project has its “good-enough”: sometimes the priority is a quick response but in other cases the covering is the focus.

 Maybe it is better to reduce the scope of the model to the portion of the data where it better works and, in the future, expand the coverage of the model when the startup has better financial resources.

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