The Enterprise Guide to Machine Learning

The field of AI is developing at a rapid pace, and hardly any company is (or should be) able to tackle all the issues on its own. A systematic approach to the make-or-buy decision is needed. However, to date most companies have not approached this question systematically at all or, even worse, they have simply delegated this decision to their standard IT purchasing process.

According to Gartner, most AI projects fail and around 80% never reach production. This guide is an attempt to share the experiences and insights of ML practitioners in the corporate sector in Germany.

Here, the challenges and best practices that companies face in practice are discussed and explained. The guide is managed by appliedAI and reflects the commitment of appliedAI's partners to share their experiences and insights beyond the PoC stage.

This guide consists of four topics:

  • The ML lifecycle
  • ML architectures
  • Platforms for ML
  • Challenges and best practices

This report is the result of the appliedAI Enterprise ML working group and is based on the experiences of leading experts from appliedAI partner companies.

Authors of the whitepaper

  • Alexander Machado, Head of Trustworthy AI CoE at appliedAI Initiative
  • Alexander Waldmann, former colleague at appliedAI Initiative

We thank you for your contributions

  • Jörn Franke (European Central Bank)
  • Faizan Aslam and Simon-Pierre Genot (Infineon)
  • Dilek Sezgün (IBM)
  • Anant Nawalgaria (Google)
  • Dirk Wacker and Into Kemmerzell (Giesecke+Devrient GmbH)
  • Matthias Neuehofer (Baywa AG)
  • Efrem Ghebru and Michael Ksoll (EnBW)

and many more.