🇨🇳Translation in Chinese
🏷 News: If you're interested to gain some insights on ML/AI technical interviews, please check out my new machine learning interview enlightener repo.
🏷 Note: This repo is under continous development, and all feedback and contribution are very welcome 😊
Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance. Several distinct components need to be designed and developed in order to deploy a production level deep learning system (seen below):
This repo 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 Advanced KubeFlow Meetup by Chris Fregly.
Fun 😳 fact: 85% of AI projects fail. 1 Potential reasons include:
The two important factors to consider when defining and prioritizing ML projects:
The following figure represents a high level overview of different components in a production level deep learning system:
Approaches:
Platforms:
Machine Learning production software requires a more diverse set of test suites than traditional software:
[TBD]
[TBD]
[1]: Full Stack Deep Learning Bootcamp, Nov 2019.
[2]: Advanced KubeFlow Workshop by Pipeline.ai, 2019.