Develop production ready deep learning code, deploy it and scale it

Deep Learning In Production Course

In this article series, our goal is dead simple. We are gonna start with a colab notebook containing prototype deep learning code (i.e. a research project) and we’re gonna deploy and scale it to serve millions or billions (ok maybe I’m overexcited) of users.

We will incrementally explore the following concepts and ideas:

• How to structure and develop production-ready machine learning code,

• How to optimize the model’s performance and memory requirements, and

• How to make it available to the public by setting up a small server on the cloud.

But that’s not all of it. Afterwards, we need to scale our server to be able to handle the traffic as the userbase grows and grows.

In this repo, you can find the full code provided in every article. Note that the code for each lesson is selft contained and can be run independently.

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Articles:

1. Laptop set up and system design
2. Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
3. How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
4. Logging and Debugging in Machine Learning
5. Data preprocessing for deep learning
6. Data preprocessing for deep learning (part2)
7. How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch
8. Deploy a Deep Learning model as a web application using Flask and Tensorflow
9. How to use uWSGI and Nginx to serve a Deep Learning model
10. How to use Docker containers and Docker Compose for Deep Learning applications
11. Scalability in Machine Learning: Grow your model to serve millions of users
12. Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly

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