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AI Operations

Intelligence Layer

The Intelligence Layer provides functionality to train, test, use, maintain and update analytics and machine learning models in the continuum, with the goal of supporting and augmenting the operations and performance of the security and meta-kernel layers by considering specific policies in the use of data and models, with a focus in trustworthiness, including:

  • Intelligence Layer Coordination: Coordination enables optimization and predictive analytics and machine learning models and its use across the continuum. This will include policies for the use, share and update of models across the edge-cloud continuum, including federated learning strategies.

  • Data Processing: Data processing and storage in formats and databases optimized for the application of analytics tasks depending on the resources available of the hosting device in the continuum.

  • AI Analytics: A library of optimized machine learning algorithms for the training and testing of predictive and optimization models, including deep learning, adaptive machine learning and reinforcement learning libraries optimized to operate in constrained devices. The ICOS Gitlab repository to manage all algorithms and libraries employed in the different version of the meta-OS will be maintained, such that, depending on the constraint of the device, only the required functionality will be fetched at installation or update time.

  • AI Models Marketplace: A collection of pre-trained analytics and ML models to be reused, updated, refined and combined to foster the application of new AI techniques in the different layers of the ICOS meta-OS. Functionality to train and compress these models for operation in constrained devices will be provided. The models will be stored in the so-called ICOS AI Models Marketplace.

  • Trustworthy AI: Provide specific algorithms to analyze the models conforming to policies for privacy and trustworthiness. Functionality for models to be trained in a federated learning fashion to ensure data protection in datasets containing user-specific data will be provided as well as explainable AI algorithms to provide reassurance of output of models to the different layers in ICOS.

Additionally ICOS provides containers to support ICOS users.

AI Support: AI support containers for ICOS users. These containers provide a consistent environment for running AI workloads, using the same library versions as the Intelligence controller. This allows users to train models and contribute them to the AI Models and Data Repository, continuously expanding the available online models. Additionally, these containers will facilitate seamless integration with data management libraries like dataClay, enabling AI offloading across the ICOS infrastructure.