Scaling MLflow for enterprise AI: What’s New in SageMaker AI with MLflow

Today we’re announcing Amazon SageMaker AI with MLflow, now including a serverless capability that dynamically manages infrastructure provisioning, scaling, and operations for artificial intelligence and machine learning (AI/ML) development tasks. It scales resources up during intensive experimentation and down to zero when not in use, reducing operational overhead. It introduces enterprise-scale features including seamless access management with cross-account sharing, automated version upgrades, and integration with SageMaker AI capabilities like model customization and pipelines. With no administrator configuration needed and at no additional cost, data scientists can immediately begin tracking experiments, implementing observability, and evaluating model performance without infrastructure delays, making it straightforward to scale MLflow workloads across your organization while maintaining security and governance.

In this post, we explore how these new capabilities help you run large MLflow workloads—from generative AI agents to large language model (LLM) experimentation—with improved performance, automation, and security using SageMaker AI ...