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Creating a pre-processing service — Machine Learning Zoomcamp Module 10

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mlz-module-10

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turn jupyter notebook into flask app
the notebook communicates with the model deployed with tensorflow
the notebook fetches an image, pre-processes it, turns it into protobuf, sends it to tensorflow-serving, does post-processing and finally gives a human-readable answer
convert notebook into python script and call the script gateway
prepare request, send request, prepare response
you can reuse the flask app code from session 5
two components: docker container with tensorflow serving and flask application with the gateway
be aware of the library sizes: tensorflow 1.7 GB, tensorflow CPU ~400 MB, tensorflow serving
turn numpy array into protobuf format
tensorflow protobuf

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