Machine Learning in Production: Keras, Flask, Docker and Heroku

Machine Learning in Production: Keras, Flask, Docker and Heroku

Pipeline for ML/DL solutions: Build the model, create an API to interact with it, containerize it and deploy it.

In this tutorial we will try to walk together through all the building blocks of a Machine/Deep Learning project in production, i.e. a model that people can actually interact with.

Broadly speaking, we’ll create a web interface in which a user could upload an image, then a bit of Deep Learning magic comes into play, and Bingo! we get a text revealing what does that image represent.

I KNOW, it’s not rocket science, it’s just image recognition, I haven’t reinvented the wheel. In fact I’m going to be even lazier and .. guess what ! .. I will be using an already trained model 😜

Keep in mind that the idea behind this tutorial is not to teach you Deep Leaning but rather to explore the pipeline of DL in production. What really matters is when we’ll be creating an API to interact with our model, “Dockerizing” it and deploying it.

The codes used in this tutorial are available on my GitHub [here].

GO ! GO ! GO !

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