This guide is an attempt to share the experience of ML practitioners in the enterprise sector. Here we discuss and elaborate on the challenges companies face in the real world. Maintained by the appliedAI Initiative, the guide reflects a commitment from the partners of appliedAI to share their experiences and best practices when moving beyond the PoC. The purpose of the guide is to build shared wisdom.

The ML lifecycle

ML software architecture

Platforms and services for ML

Challenges and Best Practices

Changelog

Contributors

Sources

This also means that this wiki is never complete and never finished. It is a living artifact that will be expanded over time and is often based on anecdotal evidence and practitioners opinions. Reach out to appliedAI to contribute or comment. Note that it is not our purpose here to reason about a specific model architecture or to highlight the latest metric-pushing advancements in the academic domain. This guide is designed to solve problems that tend to arise outside of academic ML research, that is, to address most notably questions such as how to build these probability-based systems, how to architect them, and how to scale and manage tasks in large teams comprising hundreds of developers. We shed light on the ML lifecycle in the enterprise, software architectures that support that lifecycle, and the platforms that implement such architectures. This guide was built with the help of numerous individuals, all representing companies that are pushing the boundaries of applied ML.

Structure of this guide

The topics are presented in hierarchical order, each informing the next and ultimately answering the question of which tool to choose. We move from the view of the ML lifecycle to the architectures supporting that view and ultimately to the tools that implement these architectures.

Authors

In alphabetical order:

Partners and companies that contributed to this guide

This guide was written with the help of many German industry companies as well as a number of tech companies that shape the domain as part of a working group at appliedAI. All partners of appliedAI have contributed in one or another way, we wish to thank these companies.

Additionally, a number of individuals from these companies have put in extra effort to make this guide available to the public. We want to thank these individuals and their companies (recognizing that some of these individuals and some companies wish to remain anonymous and cannot be named here).

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License

All content on these pages is Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0); all details are provided at https://creativecommons.org/licenses/by-nc-sa/4.0/.

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