Ceit has a data analysis and AI laboratory, with equipment and software tools to be able to carry out the necessary phases to develop and put into production developments that integrate ML/AI algorithms: - Descriptive data analysis - Search for critical operating parameters - Optimal design of experiments - Digital twins - MLOps/DevOps technologies - On-premise/cloud deployments These processes are applied to solve a variety of problems applied to different sectors. For example, they allow implementing solutions for anomaly detection (in machines, in manufacturing processes, in network traffic...), predictive algorithms (failures, attacks, maintenance management...), optimization algorithms, even creation of digital twins based on synthetic data for their integration in decision support systems (analysis of “what if... else” scenarios, recommenders, etc.), as already applied in the Manufacturing, Transport and Water sectors.
GPU Servers
GPU computing servers for training complex and computationally demanding models such as deep learning models.
HPC cluster
Access to distributed computing HPC cluster.
Generation of algorithms (data-driven or hybrid) for decision making, application of ML/DL techniques for the detection of anomalies and failures of processes and assets.
Data analysis techniques are used for the extraction of main parameters, complex relationships between variables, approaching strategies for data exploitation, monitoring, development and deployment of advanced diagnostic and predictive algorithms.