The development of functions is one of the most important parts of building machine learning models.

Metarank greatly simplifies this step due to its YAML configuration, so you can configure the model without a single line of code.

Features of Metarank, why is it needed?

Functions are what makes machine learning models work. Algorithms, such as LightFM, take the functions you do and calculate the values ​​for each function in order to create a resulting model. Without signs, the algorithm does not know what data to take to build a model.

How difficult is it to develop such functions?

Traditionally, the development of functions is one of the complex parts of the Moscow Region, when it comes to creating models of machine learning and personalization in general. In addition to determining what functions can be useful for your use of the model, each function should be implemented in the code, and the data should be stored somewhere.

Despite the fact that there are many open source tools that solve the problem of storage, creating and updating functions is still a problem.

When it comes to the development of functions, Metarank solves both problems: it provides a built -in storage of functions that provides all the necessary features, but, more importantly, eliminates the burden of writing code for calculating functions.

METARANK offers not only simple functions, such as a numerical extractor, but also quite complex, such as an extractor of estimates for calculating CTR and other indicators.

Instead of writing hundreds of lines of code in your favorite language (most likely on Python), you will need only a few lines of code to configure each function with Metarank:

In the next series, we will consider strategies for developing functions with real configuration examples that you can copy and start using METARANK for your use options, whether on social networks, delivery services, e -commerce or any other application where you need to configure certain elements for your users.

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