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AI-Ops Framework for Automated, Intelligent and Reliable Data/AI Pipelines Lifecycle with Humans-in-the-Loop and Coupling of Hybrid Science-Guided and AI Models

Project description

Automating and improving data-AI pipelines

The EU-funded AI-DAPT project aims to make AI solutions more reliable, trustworthy and fair by focusing on the data-centric aspects of AI development. As such, it will develop a framework of AI for information technology operations (AIOps) to automate and support the lifecycle management of data-AI pipelines that continuously learn and adapt. Using explainable AI, it will create sophisticated data operations based on a human-in-the-loop approach to ensure human involvement throughout the process. The resulting AIOps framework will be applied to real-world problems in industries such as health, robotics, energy and manufacturing, and integrated into existing open-source and commercial AI solutions in the market.

Objective

AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and adapt based on their context. AI-DAPT will design a novel AI-Ops / intelligent pipeline lifecycle framework cross-cutting the different business, legal/ethics, data, AI logic/models, and system requirements while always ensuring a human-in-the-loop (HITL) approach across five axis: “Data Design for AI”, “Data Nurturning for AI”, “Data Generation for AI”, “Model Delivery for AI”, “Data-Model Optimization for AI”. AI-DAPT will contribute to the current research and advance the state-of-the-art techniques and technologies across a number of research paths, including sophisticated Explainable AI (XAI)-driven data operations from purposing, harvesting/mining, exploration, documentation and valuation to interoperability, annotation, cleaning, augmentation and bias detection; collaborative feature engineering minimizing the data where appropriate; adaptive AI for model retraining purposes.
Overall, AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions.
In order to demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two, interlinked axes:
I. Through their actual application to address real-life problems in four (4) representative industries: Health, Robotics, Energy, and Manufacturing;
II. Through their integration in different AI solutions, either open source or commercial, that are currently available in the market.

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Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-CL4-2023-HUMAN-01-CNECT

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Coordinator

ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS
Net EU contribution
€ 916 875,00
Address
ARTEMIDOS 6 KAI EPIDAVROU
151 25 MAROUSSI
Greece
Region
Αττική Aττική Κεντρικός Τομέας Αθηνών
Activity type
Research Organisations
Links
Total cost
€ 916 875,00

Participants (17)