Die SATURN 2020 findet dieses Jahr vom 11. bis 14. Mai in Orlando statt. Das Programm ist wieder voller Highlights für Softwarearchitekten. Wir freuen uns sehr, auch mit zwei Vorträgen dabei sein zu dürfen.
Most of the time, machine learning (ML) is strongly viewed from a data science perspective. This means, you can find tons of information on algorithms and the treatment of data. However, what it actually means to architect systems, in which ML plays a role, is rather rarely found. Our focus is on the engineering of typically large systems, which are, to some degree, basing their functionality on machine learning. As such systems often serve in production large amounts of users, the fulfillment of quality attributes is critical and needs consideration in architecture design.
In this talk, we systematically decompose in the language of software architects what it means to build a system based on machine learning. We outline an architectural design space and discuss central architecture decisions an architect has to make when designing a system based on ML.
- This includes a perspective on both, the development time, and the runtime.
- We show how a system can be decomposed and how machine learning components look like and behave in the context of an overall system.
- Machine learning is fundamentally depending on data: ; Thus, the data aspect is central in our architectural considerations.
- As neural networks are very widespread nowadays for the realization of ML-based systems, we take a closer look at their architectural implications.
- We include a perspective on the activities around data collection, preparation, model selection and training, and model inference.
- We discuss deployment options for model training and model inference.
- We discuss different types of technologies available for machine learning, from as-a-service APIs over pre-trained models down to pure libraries requiring to construct and to the train the full model.
With this overview, architects will get the big picture of designing ML-based systems and have a much better position to bridge the gaps between data scientists, data engineers, and software developers and architects.
[Speakers: Dominik Rost, Matthias Naab]
Digital ecosystems and platform economy are based on a strong interconnection across organizations and allow for completely new business models. They conquer more and more areas of business and private life and companies feel the pressure to reason about new opportunities.
An integrated perspective on business, technological, and legal aspects is needed. However, there is no clear method how to shape such new digital ecosystems, neither in the business world nor in the software world.
While everyone acknowledges that this is a challenging task it often remains the question who could do it. We found that software architects can be promising candidates for ecosystem shaping as they should be used to seeing the big picture and designing in the large. However, what is needed goes far beyond the standard skill set of software architects.
We report from ecosystem projects and our experiences across many domains like banking, automotive, farming, smart city, etc. Our goal is to:
- make the audience interested in the world and business of digital ecosystems
- give them a clear terminology to talk about digital ecosystems and the describe them
- show architects what new challenges could be in front of them and how they can develop their career path
- demystify the term “platform”: everything seems to be a platform — we present a proven classification of platform terms that are helpful in the context of digital ecosystems
- give dos and don’ts in the shaping of digital ecosystems
- outline new roles that are needed when creating digital ecosystems
[Speakers: Marcus Trapp, Matthias Naab]