-
Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. Link to article: …
Read More -
This post demonstrates how to use the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment. Link to article: …
Read More -
In this post, you will learn about bi-directional streaming on AgentCore Runtime and the prerequisites to create a WebSocket implementation. You will also learn how to use Strands Agents to implement a bi-directional streaming solution for voice agents. Link to article: …
Read More -
In this post, we'll explore the new capabilities and core concepts that help organizations track and manage models development and deployment lifecycles. We will show you how the features are configured to train models with automatic end-to-end lineage, from dataset upload and versioning to model fine-tuning, …
Read More -
In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress. Link to article: …
Read More -
Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain control as AI applications multiply? Link to article: …
Read More -
In this post, we explore how Tata Power CoE and Oneture Technologies use AWS services to automate the inspection process end-to-end. Link to article: https://aws.amazon.com/blogs/machine-learning/how-tata-power-coe-built-a-scalable-ai-powered-solar-panel-inspection-solution-with-amazon-sagemaker-ai-and-amazon-bedrock/
Read More -
In this post, we'll show how the TwelveLabs Marengo embedding model, available on Amazon Bedrock, enhances video understanding through multimodal AI. We'll build a video semantic search and analysis solution using embeddings from the Marengo model with Amazon OpenSearch Serverless as the vector database, for semantic …
Read More -
In this post, we introduce checkpointless training on Amazon SageMaker HyperPod, a paradigm shift in model training that reduces the need for traditional checkpointing by enabling peer-to-peer state recovery. Results from production-scale validation show 80–93% reduction in recovery time (from 15–30 minutes or more to …
Read More -
Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource …
Read More -
In this post, we explore two powerful orchestration patterns implemented with Strands Agents. Using a common set of travel planning tools, we demonstrate how different orchestration strategies can solve the same problem through distinct reasoning approaches, Link to article: …
Read More -
In this first part of our two-part series, you'll learn how to evolve your existing DevOps architecture for generative AI workloads and implement GenAIOps practices. We'll showcase practical implementation strategies for different generative AI adoption levels, focusing on consuming foundation models. Link to article: …
Read More -
In this post, we present practical techniques and recommendations for optimizing throughput in ML training workloads that read data directly from Amazon S3 general purpose buckets. Link to article: https://aws.amazon.com/blogs/machine-learning/applying-data-loading-best-practices-for-ml-training-with-amazon-s3-clients/ …
Read More