What is machine learning infrastructure?

Machine learning infrastructure is essential for developing, deploying, and managing ML models, encompassing computing resources like CPUs and GPUs, storage systems, orchestration platforms, CI/CD pipelines, and APIs for model serving. Platforms such as Northflank offer comprehensive solutions with GPU orchestration, automated job scheduling, scalable storage, and integrated CI/CD workflows, streamlining the ML lifecycle from experimentation to production. A key insight is that while only about 10% of code relates to the ML model itself, the remaining 90% involves infrastructure code for data processing, deployment, and model serving. As AI becomes pivotal for competitive advantage, understanding ML infrastructure is crucial for engineering teams. Cloud infrastructure enhances development by offering elastic scaling, multi-cloud flexibility, and managed services, reducing the complexity of managing physical hardware. Northflank provides a managed Kubernetes experience, integrating compute orchestration, job scheduling, storage management, and development environments, while supporting deployment across multiple cloud providers. Selecting the right ML infrastructure involves balancing performance, cost, and operational complexity, with platforms like Northflank reducing these challenges by offering end-to-end capabilities.