Machine Learning Engineering
Machine Learning Engineering – 10 fundamentale Praktiken
In diesem Vortrag stellt Larysa Visengeriyeva fundamentale Praktiken für Machine Learning Engineering vor, die dabei helfen, das eigene Projekt zum Erfolg zu bringen.
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Links and Resources
- Machine Learning Canvas: A framework to design Machine Learning systems
- DDD Knowledge Crunching Methods
- Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
- What is the Team Data Science Process?
- There's No AI (Artificial Intelligence) without IA (Information Architecture)
- Machine Learning Testing: Survey, Landscapes, and Horizons
- Data Version Control
- AI Fairness 360 Open Source Toolkit (IBM)
- Responsible AI (Microsoft)
- Responsible AI (Google)
- MLOps
INNOQ Library
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Domain-Driven Design Referenz - Definitionen & Muster
Domain-driven Design spielt beim Entwerfen und Umsetzen von fachlich anspruchsvollen Systemen eine entscheidende Rolle. Für das Verständnis von DDD ist die DDD-Referenz von Eric Evans eine unverzichtbare Quelle – und liegt mit diesem Buch erstmalig in deutscher Übersetzung vor. Sowohl die Originalversion, als auch die Übersetzung stehen unter Creative-Common-Lizenz.
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Hands-on Domain-driven Design – by example
Domain-driven Design practically explained with a massive case study.
This book aims to explain the concepts of Domain-driven Design in a way that it is easily applicable in practice. Each chapter contains a theoretical part which is explained from the point of view of practical applicability and then exercises with solutions based on a comprehensive, complex case study (real estate loans).
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