{"id":"machine-learning-ml-infrastructure","license":"CC-BY-4.0 (Data: AintOnlyAI https://aintonlyai.world)","type":"technology","name":"Machine Learning (ML) infrastructure","slug":"machine-learning-ml-infrastructure","aliases":[],"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.","website":"","country":"","founded":null,"status":"active","categories":["compute","ai_infrastructure_software","data_centers","networking"],"subcategories":[],"tags":[],"metrics":{"ai_revenue_exposure":"Enterprises of all sizes and across industries depend on ML infrastructure to develop, train, and deploy AI models, driving demand for GPUs, TPUs, data pipelines, and MLOps platforms."},"confidence":0.8,"ai_adjacency_score":53,"criticality_score":50,"visibility":"public","ticker":null,"created_at":"2026-09-10T16:33:23.718Z","updated_at":"2026-09-10T16:33:23.718Z","last_researched_at":"2026-09-12T02:15:31.421Z","last_verified_at":null,"next_research_at":"2026-10-12T02:15:31.421Z","overdue":true}