1. An AWS Lambda ML Model Deployment

    Sun 10 November 2019

    In the last few years, a new cloud computing paradigm has emerged: serverless computing. This new paradigm flips the normal way of provisioning resources in a cloud environment on its head. Whereas a normal application is deployed onto pre-provisioned servers that are running before they are needed, a serverless application's codebase is deployed and the servers are assigned to run the application as demand for the application rises.

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  2. A Task Queue ML Model Deployment

    Thu 24 October 2019

    When building software, we may come across situations in which we want to execute a long-running operation behind the scenes while keeping the main execution path of the code running. This is useful when the software needs to remain responsive to a user, and the long running operation would get in the way. These types of operations often involve contacting another service over the network or writing data to IO. For example, when a web service needs to send an email, often the best way to do it is to launch a task in the background that will actually send the email, and return a response to the client immediately.

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  3. A Batch Job ML Model Deployment

    Fri 20 September 2019

    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.

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  4. Using the ML Model Base Class

    Sun 28 July 2019

    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.

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  5. Improving the MLModel Base Class

    Wed 12 June 2019

    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.

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