24 articles tagged with “mlops”.
SageMaker HyperPod now supports model caching for inference, pre-loading model weights and container images onto cluster nodes so pods read from local NVMe instead of downloading over the network.
This piece discusses the rebuilding of AUTOMATIC1111 using Gradio Workflow to enhance development processes.
Amazon SageMaker Feature Store introduced the UpdateRecord API, enabling updates to one or more feature values in a single call without reading or rewriting the full record.
AWS extends MLflow and SageMaker AI Model Registry sync to two cross-account governance patterns: a hub-and-spoke model centralized with AWS RAM and a hybrid model that isolates development accounts.
AWS introduced HyperPod InstantStart, an open source control plane combining Amazon EKS with SageMaker HyperPod to run guarded operations via a web interface and an AI agent.
Hugging Face has unveiled NeoMME, a new encoder designed for efficient multimodal and multilingual processing.
SageMaker HyperPod now offers managed Ray support on Amazon EKS, letting users create and monitor Ray clusters, connect notebooks, and run distributed training and accelerated inference.
An AWS blog post describes AI-powered approaches to metadata correction and harmonization, including human-in-the-loop validation and autonomous agent workflows, plus production governance considerations.
AWS walks through building a no-code fraud detection model by connecting SageMaker Canvas to Snowflake and training XGBoost without writing ML code.
The second post in an AWS multi-agent series examines patterns that let ML teams run many agentic AI systems across diverse frameworks, models, and providers without vendor lock-in.
Jumio built a centralized, real-time feature store on AWS delivering sub-100ms feature serving for fraud detection while saving roughly $120,000 annually.
A Hugging Face Blog post describes achieving 33 percentage points more utilization on the same cluster by changing the order rather than the hardware.
AWS details how Amazon Bedrock AgentCore Observability can track AI agents running outside AWS, routing traces, span metrics, and token usage to one dashboard.
This AWS guide explains how to set up CUR 2.0 with IAM principal data and use Amazon Athena and CUDOS dashboards to track and analyze Amazon Bedrock costs across an organization.
AWS describes how the SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on an existing cluster, with browser and VS Code access.
Nscale, a British AI neocloud, is acquiring Anyscale, a software startup that helps companies scale AI workloads across data centers and servers.
A Hugging Face Blog post addresses GPU management, framing idle GPUs as a costly problem akin to grounded aircraft.
AWS presents a solution for centralized monitoring of SageMaker Pipelines across AWS accounts and Regions using CloudWatch custom dashboards, backed by a CDK example on GitHub.
This article discusses the lessons learned from building the Shippy agent, focusing on design principles for effective AI agents.
The Hugging Face Blog discusses the intricacies of model routing, highlighting challenges that arise in AI systems and their impact on performance.
An AWS blog post explains four deployment patterns for serving Unsloth-quantized models on AWS infrastructure, covering EC2, SageMaker AI endpoints, EKS, and ECS.
The article delves into various profiling methods in PyTorch, emphasizing attention mechanisms in models to optimize performance.
This blog post addresses common mistakes in MCP tool design and offers practical strategies for effective context engineering.
AWS details five new inference capabilities for SageMaker HyperPod, including multi-tier data capture, direct Hugging Face Hub deployment, NVMe model loading, Route 53 DNS, and pod-level IAM.