ML.NET represents an open-source and cross-platform machine learning framework that can incorporate and use the machine learning algorithms in. The more detailed techniques for building data processing and transformation pipelines, customizing the train and test data splitting, applying the concept of cross-validation and interpretation of the model performance and evaluation are beyond the scope of the article and can be referenced via the ML.NET API. ** Note: I will design and build the solution utilizing the ML.NET Model Builder powered by the automated machine learning, or AutoML, using the intuitive and user friendly graphical Visual Studio extension. Still, it is a fully functional approach for training, building, evaluating and implementing predictive decision-based/supervised driven models within real-life testing or production deployed prototypes and applications environments.
#MODEL BUILDER ITERATE ROW SELECTION CODE#
* Note: This article’s solution design and source code are simplified to emphasize the core concepts and integration strategy in general. Besides this, I will also cover and leverage the idea of Lead Scoring as part of the created model`s prediction evaluation. As mentioned in the article, it conceptually follows the same approach and supervised ML idea, differing only in the classification-based prediction strategy. NET, I will proceed presenting the implementation of the lead decision solution as a continuation of the lead decision solution designed and implemented using the KNIME Platform. Since I have already presented the lead scoring idea in. NET, which is the technology I am professionally working with daily, in this article I want to uncover the native machine learning potential of the framework, more specifically, the ML.NET. What I have mentioned there was the opportunity to use the approach for bridging the technical differences between the different data science and application development platforms, in this case targeting the. That is a procedure applicable for integrating the trained model as part of the Web API or Console Application as well.
I described one possible way of deploying the Python-based regression model as Microsoft Azure Function. Recently, I wrote an article explaining the utilization of the ONNX format in integrating the Scikit-learn lead scoring machine learning model into the.
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