This post demonstrates how to train robot policies for the Unitree H1 humanoid using NVIDIA Isaac Lab on Amazon SageMaker AI. It explores two compute options: Amazon SageMaker HyperPod and Amazon SageMaker Training Jobs.
This blog details end-to-end encrypted ML inference using Amazon SageMaker AI and FHE. It introduces a more flexible, higher-level approach based on concrete-ml, supporting several common models and being API compatible with scikit-learn.
This post details how to get started with the NEXUS large tabular model on Amazon SageMaker JumpStart. It walks through the deployment process and demonstrates how to run predictions against enterprise datasets.
Amazon is announcing three new capabilities in SageMaker Python SDK v3.8.0 to accelerate ML feature pipelines. The post provides code examples and links to notebooks for comprehensive walkthroughs, covering Lake Formation governance and Iceberg table properties.
NVIDIA Nemotron 3 Ultra is now available on Amazon SageMaker JumpStart. This deployment offers 5x faster inference and 30% lower cost for AI workloads.
This post showcases a comprehensive observability solution utilizing Amazon Managed Grafana dashboards. It provides a holistic view of both the quality and quantity of LLMs served on Amazon SageMaker AI inference endpoints.
Azercell Telecom partnered with the AWS Generative AI Innovation Center to develop an Azerbaijani large language model (LLM) on Amazon SageMaker AI. This six-week collaboration established a production-ready framework for telecom use cases and a customer-facing chatbot, overcoming data scarcity and linguistic complexity challenges.
This content explores how to customize agentic AI models using Amazon SageMaker. It likely provides technical details and best practices for tailoring these advanced AI systems on the AWS platform.
This post demonstrates how to build a secure Flask-based MLflow proxy service providing HTTPS access to Amazon SageMaker MLflow without the SDK. The solution targets organizations undergoing cloud transformation, aiming to preserve existing ML workflows while adopting cloud-native services.
This post demonstrates how to build a secure, complete LLM fine-tuning workflow, integrating Databricks Unity Catalog with Amazon SageMaker AI and Amazon EMR Serverless. The solution securely accesses governed data, maintains lineage, and registers trained artifacts while preserving central governance and compliance.
Real-time voice applications, such as voice agents and live captioning, rely on simultaneous speech-to-text transcription. Traditional request-response inference falls short, introducing latency that hinders real-time functionality.
Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking. This update introduces powerful new features for observability and evaluation in generative AI workflows.
O autor descreve sua primeira impressão do Amazon SageMaker, inicialmente confundindo-o com um serviço genérico de ML. Ele o compreende melhor como uma plataforma gerenciada da AWS que simplifica a construção, treinamento e implantação de sistemas de aprendizado de máquina para equipes.