Softwaretechnik-Trends 42(4) - 2022 http://dl.gi.de/handle/20.500.12116/40152 2026-07-21T14:25:04Z 2026-07-21T14:25:04Z Ausschreibung Ernst Denert Software-Engineering Preis 2022 http://dl.gi.de/handle/20.500.12116/40162 2023-01-25T14:37:34Z 2022-01-01T00:00:00Z Ausschreibung Ernst Denert Software-Engineering Preis 2022 2022-01-01T00:00:00Z A High Quality Data Pipeline for Reasonable-Scale Machine Learning Faragó, David http://dl.gi.de/handle/20.500.12116/40159 2023-01-25T14:37:34Z 2022-01-01T00:00:00Z A High Quality Data Pipeline for Reasonable-Scale Machine Learning Faragó, David Data quality (especially correctness) plays a critical role in the success of a machine learning (ML) project. This paper describes a data pipeline for creating high quality data, using as example Key Information Extraction (KIE) from invoices – one of the most popular tasks in Intelligent Document Processing (IDP). The tasks of each data pipeline step are listed, showing the decisions and technology involved. The focus is on practicality: doing ML at reasonable-scale, i.e. with as little cost (people and hardware) as possible, and a concern for practice more than achieving high scores on a metric that is not grounded in practical use. Contributions: 1. an extended list of quality dimensions, with simple definitions 2. overview of a data pipeline, examplified on KIE 3. for each pipeline step a list of tasks, showing decisions, pitfalls, and technology involved 4. in particular, how to use the state of the art contrastive model CLIP to solve difficult selection and reduction tasks on images 5. a tool for labeling key information on images 6. a labeling guide for invoices. Most contributions can easily be transfered to other supervised learning tasks. 2022-01-01T00:00:00Z Have your cake and eat it: Reconciling AI and Privacy in Deutsche Telekom’s “Hallo Magenta” Digital Assistant Störrle, Harald http://dl.gi.de/handle/20.500.12116/40160 2023-01-25T14:37:34Z 2022-01-01T00:00:00Z Have your cake and eat it: Reconciling AI and Privacy in Deutsche Telekom’s “Hallo Magenta” Digital Assistant Störrle, Harald 2022-01-01T00:00:00Z Der ISTQB „Certified Tester® AI Testing“ (CT-AI) Winter, Mario http://dl.gi.de/handle/20.500.12116/40161 2023-01-25T14:37:34Z 2022-01-01T00:00:00Z Der ISTQB „Certified Tester® AI Testing“ (CT-AI) Winter, Mario Immer mehr unternehmenskritische und/oder sicherheitsrelevante Anwendungen enthalten KI-basierte Komponenten. Darüber hinaus gibt es erste erfolgreiche Anwendungen von KI zur Unterstützung des Testens. Der englischsprachige Lehrplan (syllabus) zum ISTQB „Certified Tester® AI Testing“ (CT-AI) Version V1.0 ist seit Oktober 2021 verfügbar. Seit Ende September 2022 ist nun auch der vom German Testing Board e.V. (GTB) unter Mitwirkung des Austrian Testing Board (ATB) und des Swiss Testing Board (STB) übersetzte deutschsprachige Lehrplan V1.0D verfügbar. Dieser Beitrag skizziert die Ziele und Themenfelder des Lehrplans. 2022-01-01T00:00:00Z