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UBITECH in RAIDO

Partner

UBITECH is a globally recognized, innovative software house, systems integrator, and technology provider. The company delivers cutting-edge technical solutions and consulting services to businesses, organizations, and governments, enabling secure, efficient access to diverse information resources anytime, anywhere. With a strong international presence, UBITECH operates through subsidiaries and representation offices in Limassol (UBITECH LIMITED), Madrid (Business Development Office), Buenos Aires (UBITECH SRL targeting Argentina, Paraguay, Uruguay, and Bolivia), and Guayaquil (Representation Office for Ecuador and Panama), focusing on Spanish-speaking regions in Central and Latin America.

Role in RAIDO

In the RAIDO project, UBITECH is at the forefront of defining its innovative architecture. The company is driving the development of reliable AI and data optimization systems while creating a novel data generation and AI optimization framework. This framework incorporates advanced techniques such as data curation, lifelong learning, transfer learning, few-shot learning, and feedback monitoring processes. It supports cutting-edge features like data distillation, human-in-the-loop (HITL) methodologies, explainable AI (XAI), secure data storage, and ethical AI practices to enhance energy- and data-efficient AI models.

UBITECH’s contributions include implementing RAIDO’s Data Enrichment, Curation, and Distillation pipeline to improve data quality and quantity. This involves:

  • Filtering techniques: Handling missing data, outlier detection, noise removal.
  • Feature extraction methods: Feature engineering and dimensionality reduction.
  • Balancing techniques: Oversampling, decision threshold adjustment, class weight adjustment.
  • Data transformation tools: Smoothing, normalization, attribute construction.
  • Annotation tools: For image data (e.g., keypoint tools, auto-annotation) and text/timeseries data (e.g., semantic annotation).

Additionally, UBITECH is delivering RAIDO’s Information Layer by implementing Data Lakes designed for efficient AI training. This ensures improved performance in both training and deploying AI models.

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