Machine Learning techniques increasingly prove to be helpful in different businesses and sectors. However, applying them in organizations does not consist of developing and training models but also in a series of previous and subsequent steps related to the definition of the use case and the target. The monitoring, once put into production and associated considerations, with its interpretability and possible biases.
In the first place, it started from the premise that, when implementing Machine Learning models, especially in the banking sector, “we need the models to be traceable, reproducible and verifiable”, as well as industrialized.
This industrialization makes it possible to standardize the processes that usually occur in all Machine Learning projects, to be agile while guaranteeing the three aspects mentioned above and reducing the cost of maintenance of the models.
The expert gave an example: “at the bank, we have to be able to answer why a person was denied a loan, tracing the path from the data to the score issued by the model.” To do this, it is necessary to know which version of the model is in production and what data was used or where the predictions were stored. Several versions of data are usually saved, associated with the models to cover the traceability and reproducibility part. Those are in production at all times.
On the other hand, verifiability is handled by a committee in which different bank areas intervene ( model owner, risks, legal, etc.). The Machine Learning model cannot go into production if the committee does not approve it. In addition, other business decisions are made: decision thresholds, when to launch or when to retrain the model. Check out this Best Machine Learning Course, taught by industry experts who have mastered this domain and have many years of experience in the industry.
As Experts explained, the design and development of a Machine Learning model are governed by a series of requirements: that it be simple, monitorable, interpretable, that it is not biased, that the input variables comply with the regulation and that it is adjusted to the case usage and operational restrictions.
All this means taking into account some aspects and addressing some challenges in the different phases of the process:
As we can see, a Machine Learning project in the company cannot be limited to developing and training a helpful model. It is necessary to attend to a series of considerations before and during the process: for example, that the models fit the objective, but that they can also be generalized to be more efficient or not lose sight of legal or ethical issues.
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