The article describes a model of cloud-native AI pipelines designed for continuous optimization of computing infrastructure and real-time anomaly detection. The developed model combines modern approaches to observability, machine learning (ML), and auto-scaling   based   on   load forecasting.  The methodology is based on the use of LSTM models, autoencoders, and convolutional neural networks (CNN) integrated into Kubernetes environment with support for Prometheus, Kafka, and Grafana. Load changes are simulated, and the system's response to critical events is evaluated. The results demonstrate a significant improvement in anomaly detection accuracy (up to 93%) and resource efficiency (up to 26% cost reduction compared to traditional approaches). The proposed model can be used in AIOps systems that require a high level of automation and reliability.

The article discusses the solution to the issue of information systems architecture, which makes it possible to separate identification flows associated with the processes of managing the identification of digital entities and data flows associated with the processes of managing data describing the properties of digital entities. At the same time, a new architecture of connections between digital entities and services is proposed, which makes it possible to create a flexible system for processing the properties of identifiers that can be described in an irregular and unstructured form. In this case, the nomenclature of the parameters of the properties of a digital entity can be customized and expanded as a separate entity, which always maintaining a connection with the identifier. This allows, within the framework of the information system, on the one hand, to adapt any identification systems and ensure the solution of convergence requirements, to ensure compliance and solution of the requirements of recognition, immutability, stability in conditions of processing large volumes of data without implementing generally accepted principles of a global unique permanent identifier, and on the other hand, build flexible connections between digital entities and services.