Note: I'm currently taking a break from this course to focus on my studies so I can finally graduate
Zahorecz Tibor ZahoreczTibor
ZahoreczTibor
/ 0-startup-overview.md
Created
November 4, 2017 16:01
— forked from dideler/0-startup-overview.md
Startup Engineering notes
ZahoreczTibor
/ how-to-make-a-racist-ai-without-really-trying.ipynb
Created
July 24, 2017 12:13
— forked from rspeer/how-to-make-a-racist-ai-without-really-trying.ipynb
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ZahoreczTibor
/ Decision tree tutorial v1.ipynb
Created
June 23, 2017 12:14
— forked from anonymous/Decision tree tutorial v1.ipynb
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ZahoreczTibor
/ test_import.md
Created
June 1, 2017 11:14
— forked from avloss/test_import.md
TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:
- Tutorial examples have C++ code (which I don't know)
- Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
- It needs to be compiled. That process takes forever!
After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.