  Systems with artificial intelligence components, so-called AI-based systems,
have gained considerable attention recently. However, many organizations have
issues with achieving production readiness with such systems. As a means to
improve certain software quality attributes and to address frequently occurring
problems, design patterns represent proven solution blueprints. While new
patterns for AI-based systems are emerging, existing patterns have also been
adapted to this new context.
  The goal of this study is to provide an overview of design patterns for
AI-based systems, both new and adapted ones. We want to collect and categorize
patterns, and make them accessible for researchers and practitioners. To this
end, we first performed a multivocal literature review (MLR) to collect design
patterns used with AI-based systems. We then integrated the created pattern
collection into a web-based pattern repository to make the patterns browsable
and easy to find.
  As a result, we selected 51 resources (35 white and 16 gray ones), from which
we extracted 70 unique patterns used for AI-based systems. Among these are 34
new patterns and 36 traditional ones that have been adapted to this context.
Popular pattern categories include "architecture" (25 patterns), "deployment"
(16), "implementation" (9), or "security & safety" (9). While some patterns
with four or more mentions already seem established, the majority of patterns
have only been mentioned once or twice (51 patterns). Our results in this
emerging field can be used by researchers as a foundation for follow-up studies
and by practitioners to discover relevant patterns for informing the design of
AI-based systems.
