{"@context":"https://schema.org","@type":"Product","license":"https://creativecommons.org/licenses/by/4.0/","creditText":"Data: AintOnlyAI (https://aintonlyai.world), CC-BY-4.0","identifier":"machine-learning-ml-infrastructure","name":"Machine Learning (ML) infrastructure","description":"The underlying technology stack and resources necessary to support the development, deployment, and management of machine learning models and applications. It includes MLOps, specialized hardware (GPUs, TPUs), software frameworks, data storage systems, networking components, and optimized data pipelines designed for high-performance AI workloads. Only about 10% of code in a production ML system is the ML model itself; the remaining 90% is infrastructure code handling data processing, deployment, monitoring, and serving.","additionalType":"technology","keywords":"compute, ai_infrastructure_software, data_centers, networking","additionalProperty":{"@type":"PropertyValue","name":"ai_adjacency_score","value":53}}