Our main goal to complete by the end of the chapter would be to standardize the toolset to work together and achieve consistent accurate results.
In the process of building applications using Deep Learning (DL) algorithms which can also scale for production, it's very important to have a right kind of setup on local/cloud to make things work end to end. So in this chapter, we will learn how to setup a DL environment which we will be using to run all the experiments and finally take the AI models into production.
STRATEGY TIP: First, we will discuss the major components which are required to code, build and deploy the DL models, then various ways to do it and finally few code snippets which will help to automate the whole process.
Here is the list of required components which we need to build DL applications:
- Ubuntu 16.04 or greater
- Anaconda Package
- Python 2.x/3.x
- Deep Learning packages: TensorFlow/Keras
- CUDA for GPU support
- Gunicorn for deployment at scale