The output from the model-building steps are trained models that are packaged as Java objects. The stages consist of (a) extracting the training dataset from the operational online data, (b) preprocessing the dataset into training-dataset “vectorized” format, and (c) training and evaluating the trained predictive model before it is moved into production. In my second post, I discussed the Apache Ignite model-building stages. In my first post, I introduced Apache® Ignite™ machine learning and explained how it delivers large-scale, distributed, machine-learning (ML) workloads.
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For post 1 click here and for post 2 click here. Note: This is the third and final post in the blog series: Continuous Machine Learning at Scale With Apache Ignite.
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