machine learning

How to build an API using Flask and deploy it to the cloud using Gunicorn

How to build an API using Flask and deploy it to the cloud using Gunicorn Introduction: What is HTTP and What Does it Have to do with Flask? HTTP is the protocol for websites. The internet uses it to interact and communicate with computers and servers. Let me give you an example of how you use it everyday. When you type the name of a website in the address bar of your browser and you hit enter.

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demand and capacity mismatch

We may still remember as we were standing at end of a long line in a department store waiting for a cashier to check out our groceries, or sitting beside an empty table waving to a busy waitress to come over and take our orders. In the department store, we were already ready to pay for our goods which placed in our shopping carts. While because the cashier was not ready to serve us, we had to waste our time in waiting the service from the cashier.

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Deploying Machine Learning Models in Production with Flask

Introduction After we generate the Machine Learning models and tuning and evaluating them, we would then consider to apply the model into production. The blog here will show the steps to put the models we trained to production environments. python environment setup # install Anaconda yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver download Anaconda from https://www.anaconda.com/distribution/#linux bash ~/Downloads/Anaconda3-2019.10-Linux-x86_64.sh ln -s [python under anaconda/bin/] /usr/bin/python ln -s [pip under anaconda/bin] /usr/bin/pip # install flask and gunicorn source ~/.

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