Usually, artificial intelligence models start training with random variables and adjust according to the data of the training set. But randomness is not what business nes in the real world. Therefore, for the reproducibility of results in machine learning, a number of standard approaches are us, such as fixing a random initial number random_state. Roughly speaking, when the generat pseudo-random variables will have the same value on each call. However, transferring the train model to the operating environment requires separate storage of input data, their transformations, and features obtain from the data. After all, the problem is that the variables that enter the model are not the features that it uses in its calculations.
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For example, a date can be divid into a day of the week, a month, and a weekend sign. Clear structuring and preservation of the sequence of extraction of features from the input data will allow you to repeat actions with the data and obtain the Czech Republic Phone Number List expect results. An alternative approach Also, a good approach for building industrial machine learning systems is to build a reproducible end-to-end pipeline for all stages of building a CV solution, which include: data preparation; building a model; model training and evaluation; deploying the train model in an industrial environment.
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Pipeline steps are describ using solutions such as Kubeflow Pipelines, MLFlow, and others. At the same time, it is necessary to ensure that the data entering the model does not shift in relation to the characteristics that were initially present BRB Directory during the training of the model. After all, the AI model only reproduces the patterns of new data found in the initial data set. This process data drift – data drift is subject to constant monitoring and timely notifies specialists bas on data for the analysis of a specific non-standard situation. Essentially, the model is deploy to make prictions bas on data that it the model has not seen during the training process.