Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance. This post aims to be an engineering guideline for building production-level deep learning systems that will be deployed in real-world applications. The material presented here is borrowed from Full Stack Deep Learning Bootcamp (by Pieter Abbeel at UC Berkeley, Josh Tobin at OpenAI, and Sergey Karayev at Turnitin), TFX workshop by Robert Crowe, and Pipeline.ai's Fun fact: 85% of AI projects fail. In the following, we will go through each module and recommend toolsets and frameworks as well as best practices from practitioners that fit each component.

Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance. This post aims to be an engineering guideline for building production-level deep learning systems which will be deployed in real world applications. The material presented here is borrowed from Full Stack Deep Learning Bootcamp (by Pieter Abbeel at UC Berkeley, Josh Tobin at OpenAI, and Sergey Karayev at Turnitin), TFX workshop by Robert Crowe, and Pipeline.ai's Fun fact: 85% of AI projects fail. In the following, we will go through each module and recommend toolsets and frameworks as well as best practices from practitioners that fit each component.