Machine Learning Engineering
Der allgegenwärtige Hype um Machine Learning und Artificial Intelligence kann den Eindruck vermitteln, dass Software mit ML-Modellen einfach zu entwickeln ist. Laut diversen Studien scheitern allerdings fast 80 % der ML-Projekte.
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