OUR BLOGS
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Introducing AI-DAPT Components: The Data Valuation Engine
The Data Valuation Engine helps assess data quality through correlation, feature importance, and spatial bias analysis for more trustworthy AI.
Introducing AI-DAPT Components: Model Observability and Adaptive AI Services
The Model Observability and Adaptive AI Services monitor model performance, detect drift, and support continuous AI adaptation.
Introducing AI-DAPT Components: The Synthetic Data Generation Engine
The Synthetic Data Generation Engine creates and evaluates synthetic datasets, enabling safe, representative data for trustworthy AI development.
Introducing AI-DAPT Components: The Data Cleaning Engine
The Data Cleaning Engine detects anomalies and repairs missing or corrupted data, helping prepare high-quality datasets for trustworthy AI.
Introducing AI-DAPT Components: The Data Features Toolkit
The Data Features Toolkit simplifies feature engineering through visual, no-code workflows that transform raw data into AI-ready datasets.
The AI-DAPT Platform Alpha Is Here
The AI-DAPT Alpha release delivers an integrated platform for trustworthy AI, combining data pipelines, human oversight, explainability, and hybrid AI
Partner Spotlight: MCS
MCS Data Labs GmbH is a Berlin-based technology firm founded in 2013, specializing in turning complex data into actionable insights.
Partner Spotlight: WITSIDE
Based in Greece, WITSIDE is a premier software and consulting house dedicated to "Intelligence for Business."
UCY’s Partner Spotlight
Within UCY, the Laboratory for Internet Computing (LInC) of the Department of Computer Science leads the universityโs participation in the
UNINOVAโs Partner Spotlight
Pioneering AI Governance, Innovation, and Impact UNINOVA โ Instituto de Desenvolvimento de Novas Tecnologias โ is a multidisciplinary, independent, and
UNIVERSITAT POLITรCNICA DE CATALUNYA PARTNER SPOTLIGHT
UPC contributes to AI-DAPT through its CRAAX Lab, leading AI pipeline monitoring and delivery. With strong expertise in ICT, AI,
SETU Partner Spotlight
Building on this foundation, SETU will lead Task 4.4, designing a comprehensive software validation and verification framework for all AI-DAPT
Introducing AI-DAPT Components: The Data Valuation Engine
The Data Valuation Engine helps assess data quality through correlation, feature importance, and spatial bias analysis for more trustworthy AI.
Introducing AI-DAPT Components: Model Observability and Adaptive AI Services
The Model Observability and Adaptive AI Services monitor model performance, detect drift, and support continuous AI adaptation.
Introducing AI-DAPT Components: The Synthetic Data Generation Engine
The Synthetic Data Generation Engine creates and evaluates synthetic datasets, enabling safe, representative data for trustworthy AI development.
Introducing AI-DAPT Components: The Data Cleaning Engine
The Data Cleaning Engine detects anomalies and repairs missing or corrupted data, helping prepare high-quality datasets for trustworthy AI.
Introducing AI-DAPT Components: The Data Features Toolkit
The Data Features Toolkit simplifies feature engineering through visual, no-code workflows that transform raw data into AI-ready datasets.
The AI-DAPT Platform Alpha Is Here
The AI-DAPT Alpha release delivers an integrated platform for trustworthy AI, combining data pipelines, human oversight, explainability, and hybrid AI
Physics-Informed Hybrid Energy Management
A physics-informed hybrid model detects and reconstructs cyberattacks on power grid data, improving resilience and operational reliability.
Governing Data Before It Governs AI
AI-powered data governance improves data quality, transparency, and compliance, enabling more trustworthy AI systems.
From Data Streams to Scalable Intelligence
Real-time data pipelines enable scalable, low-latency analytics, turning continuous data streams into actionable insights for adaptive AI systems.
Advancing towards the project demonstrators
How continuous testing and validation ensure reliable integration of AI-DAPT services as the platform advances toward its demonstrators.
๐๐-๐๐๐๐ ๐๐๐ฆ๐จ๐ง๐ฌ๐ญ๐ซ๐๐ญ๐จ๐ซ ๐
๐๐ฎ๐ฆ๐๐ง-๐๐๐ง๐ญ๐๐ซ๐๐ ๐๐ ๐๐จ๐ซ ๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ๐ข๐ฌ๐๐ ๐๐๐๐ฅ๐ญ๐ก๐๐๐ซ๐ Within the AI-DAPT EU Project, the Health Demonstrator MCS Datalabs in collaboration with Charitรฉ Universitรคtsmedizin
Tuning 3D Printing with Hybrid Intelligence
In 3D printing, performance fails quietly, as warped geometry, brittle layers, inconsistent strength, or a part that passes inspection but