In previous blog posts I showed how to develop an ML model in such a way that makes it easy to deploy, and I showed how to create a web app that is able to deploy any model that followed the same design pattern. However, not all deployments of ML model are deployed within web apps. In this blog post I deploy the same model used in the previous blog posts as an ETL job.
read moreUsing the ML Model Base Class
In previous blog posts I showed how to build a simple base class for abstracting machine learning models and how to create a python package that makes use of the base class. In this blog post I aim to use the ideas from the previous blog posts to build a simple application that uses the MLModel base class to deploy a model.
read moreImproving the MLModel Base Class
In the previous blog post in this series I showed an object oriented design for a base class that does Machine Learning model prediction. The design of the base class was intentionally very simple so that I could show a simple example of how to use the base class with a scikit-learn model. I showed an easy way to publish schema metadata about the model inputs and outputs, and how to write model deserialization code so that it is hidden from the users of the model. I also showed how to hide the implementation details of the model by translating the user's input to the model's input so that the user of the model doesn't have to know how to use pandas or numpy. In this blog post I will continue to make improvements to the MLModel class and the example that I used in the previous post.
read moreA Simple ML Model Base Class
When creating software it is often useful to write abstract classes to help define different interfaces that classes can implement and inherit from. By creating a base class, a standard can be defined that simplifies the design of the whole system and clarifies every decision moving forward.
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