Training and Deploying Machine Learning Models with Containers
November 18, 2019
Between ensuring that the right version Python/Pip are installed on your system, and that it doesn’t conflict with other Python/Pip versions on your system AND that when you deploy your model to the cloud that the versions of the dependencies you’ve used in your projects are still compatible with the version on your cloud-based system, it’s a wonder that we ever get any time to focus on building and training our neural networks.
Fortunately, there’s a way to ensure that all of this is a never a problem again – Containers! (specifically, Minishift )
With containers, we can create a clean, virtual environment to setup and train our neural networks in, and then deploy them at scale with the exact same same environment. No more dependency hell!
In this workshop, you will learn:
– How to build a Convolutional Neural Network (CNN) that can detect handwritten digits (with Keras and the MNIST dataset)
– How to train and deploy a CNN with the Flask web framework and Keras
– How to install and run Minishift (a locally run OpenShift cluster of one image) on your machine
– How to create a project in OpenShift
– How to create an app in OpenShift and pull the source code for application from Github
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