An AWS blog post explains how Managed MLflow on Amazon SageMaker AI automatically synchronizes models registered in MLflow into the SageMaker AI Model Registry. The workflow addresses the gap between experimentation, where data scientists track many candidate runs, and governance, where officers need one authoritative registry to validate, approve, and audit production-bound models.
AWS announced a new inference meta-monitoring system for Amazon SageMaker AI endpoints, built with Amazon Quick, to continuously track prediction and data quality metrics. This governance layer sits above production ML pipelines, helping organizations detect model issues before customers complain. The post provides design and implementation guidance for predictive models.
AWS announced a new inference meta-monitoring system for Amazon SageMaker AI endpoints, built with Amazon Quick, to continuously track prediction and data quality metrics. This governance layer sits above production ML pipelines, helping organizations detect model issues before customers complain. The post provides design and implementation guidance for predictive models.
Amazon SageMaker AI introduced a user interface for its generative AI inference recommendations feature, which was previously available only via APIs. The UI helps developers quickly find optimal instance types, serving containers, and optimization strategies for deploying generative AI models, reducing the manual benchmarking cycle.
Amazon SageMaker AI introduced a user interface for its generative AI inference recommendations feature, which was previously available only via APIs. The UI helps developers quickly find optimal instance types, serving containers, and optimization strategies for deploying generative AI models, reducing the manual benchmarking cycle.
AWS announced the SageMaker AI Spaces add-on for Amazon EKS, letting data scientists run interactive IDEs like JupyterLab and Code Editor directly on their EKS clusters. This keeps GPU nodes, shared storage, and IAM roles available to pipelines, avoiding the need to switch to standalone JupyterHub or local laptops.
AWS announced the SageMaker AI Spaces add-on for Amazon EKS, letting data scientists run interactive IDEs like JupyterLab and Code Editor directly on their EKS clusters. This keeps GPU nodes, shared storage, and IAM roles available to pipelines, avoiding the need to switch to standalone JupyterHub or local laptops.
AWS published a post demonstrating how to build agentic workflows that mix managed foundation models with custom or cost-optimized models by combining OpenAI-compatible endpoints on SageMaker AI with Amazon Bedrock AgentCore runtime. This approach lets specialized agents collaborate on complex tasks while each uses the model best suited to its job, offering cost optimization, data residency, and model flexibility.
AWS published a post demonstrating how to build agentic workflows that mix managed foundation models with custom or cost-optimized models by combining OpenAI-compatible endpoints on SageMaker AI with Amazon Bedrock AgentCore runtime. This approach lets specialized agents collaborate on complex tasks while each uses the model best suited to its job, offering cost optimization, data residency, and model flexibility.
AWS announced a redesigned script mode in the Amazon SageMaker AI SDK v3, enabling custom training and inference code without building Docker images. The update simplifies the bring-your-own-model workflow for practitioners.
AWS announced a redesigned script mode in the Amazon SageMaker AI SDK v3, enabling custom training and inference code without building Docker images. The update simplifies the bring-your-own-model workflow for practitioners.