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Context Levels in Data Science Solutioning in real-world

@machinelearnbot

Solution development: Using historical data, involves extensive experimentation, testing and validation; Solution deployment: Using the solution to get the insight and/or decision support; Solution assimilation: In the workflow enabling actions based on insight and/or prediction made by the solution; Solution maintenance and update: Periodic checking and validation of the solution performance and update to improve performance if required. Solution maintenance and update: Periodic checking and validation of the solution performance and update to improve performance if required. Solution maintenance and update: Periodic checking and validation of the solution performance and update to improve performance if required. An algorithm works with available data footprint of the process of interest; It discovers the relationships between the process characteristics and the outcomes; The above relationships are, more often than not, in form of complex patterns; Discovering these patterns require application of powerful learning algorithms on the historical data; Discovered patterns lead to learning the required model parameters; An analysis/model application algorithm use these parameters to create the model and apply it on the new data in order to compute the output.