Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although […]
Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI Read More »
Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management,
Long-running execution flows now supported in Amazon Bedrock Flows in public preview Read More »
Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models
Voice AI is changing the way we use technology, allowing for more natural and intuitive conversations. Meanwhile, advanced AI agents can now understand complex questions and act autonomously on our
Building intelligent AI voice agents with Pipecat and Amazon Bedrock – Part 2 Read More »
The global fashion industry is estimated to be valued at $1.84 trillion in 2025, accounting for approximately 1.63% of the world’s GDP (Statista, 2025). With such massive amounts of generated
As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds
Amazon SageMaker HyperPod now provides a comprehensive, out-of-the-box dashboard that delivers insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to
Amazon SageMaker now offers fully managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your generative AI journey from idea to production. This release transforms managed MLflow from
Today, we’re excited to announce that Amazon SageMaker HyperPod now supports deploying foundation models (FMs) from Amazon SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or
AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can
Supercharge your AI workflows by connecting to SageMaker Studio from Visual Studio Code Read More »
