The aim of this post is to explain Machine Learning to software developers in hands-on terms. Model is based on a common use case in enterprise systems — predicting wait time until the business report is generated.
Report generation in business applications typically takes time, it can be from a few seconds to minutes. Report generation requires time, because typically it would fetch and process many records, this process needs time. Users often get frustrated, they don’t know how long to wait until the report is finished and may go away by closing browser, etc. If we could inform user, before submitting the report request — how it long it will take to execute it, this would be great usability improvement.
I have implemented Machine Learning model using Keras regression to calculate expected report execution time, based on training data (logged information from the past report executions). Keras is a library which wraps TensorFlow complexity into simple and user-friendly API.
Python source code and training data is available on my GitHub repo. This code is based on Keras tutorial.
Report generation in business applications typically takes time, it can be from a few seconds to minutes. Report generation requires time, because typically it would fetch and process many records, this process needs time. Users often get frustrated, they don’t know how long to wait until the report is finished and may go away by closing browser, etc. If we could inform user, before submitting the report request — how it long it will take to execute it, this would be great usability improvement.
I have implemented Machine Learning model using Keras regression to calculate expected report execution time, based on training data (logged information from the past report executions). Keras is a library which wraps TensorFlow complexity into simple and user-friendly API.
Python source code and training data is available on my GitHub repo. This code is based on Keras tutorial.
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