AWS Blog

Amazon EC2 and SageMaker AI are two of the foundational AWS services for Generative AI. Amazon EC2 provides the scalable computing power needed for training and inference, while SageMaker AI offers built-in tools for model development, deployment, and optimization. Cost optimization is crucial since Generative AI workloads require high-performance accelerators (GPU, Trainium, or Inferentia) and extensive processing, which can become expensive without efficient resource management. By leveraging the below cost optimization strategies, you can reduce costs while maintaining performance and scalability.

When implementing machine learning workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an example of how to incorporate code that relies on external dependencies into your SageMaker Canvas workflows.

Today, we are announcing an enhanced private hub feature with several new capabilities that give organizations greater control over their ML assets. These enhancements include the ability to fine-tune SageMaker JumpStart models directly within the private hub, support for adding and managing custom-trained models, deep linking capabilities for associated notebooks, and improved model version management.

In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.

As we celebrate International Women’s Day (IWD) this March, I had the privilege of attending the ‘Women in Tech’ User Group meetup in Shenzhen last weekend. I was inspired to see over 100 women in tech from different industries come together to discuss AI ethics from a female perspective. Together, we explored strategies such as [...]

In this blog post, we will demonstrate how business units can use Amazon SageMaker Unified Studio to discover, subscribe to, and analyze these distributed data assets. Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems.

Today, we’re excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

One of the primary strategies for growth for partners is through global expansion. Amazon Web Services (AWS) continues to invest in infrastructure around the globe. The Middle East is one region with huge potential for growth. According to IDC, more than 75 percent of workloads are on premises in the United Arab Emirates (UAE) and Saudi Arabia. The AWS and IDC report “Unlocking the Full Potential of AI in the Middle East” points out that 28 percent of organizations surveyed in the UAE and Saudi Arabia are currently investing in AI while another 50 percent plan to invest. Read this post to learn more.

If you or your organizations are in the midst of exploring generative AI technologies, it’s important for you to be aware of the investment that comes with these advanced applications. While you are aiming at the expected return on your generative AI investment, such as, operational efficiency, increased productivity, or improved customer satisfaction, you should also have a good understanding of levers you can use to drive cost savings and enhanced efficiency. To guide you through this exciting journey, we will publish a series of blog posts filled with practical tips to help AI practitioners and FinOps leaders understand how to optimize the costs associated with your generative AI adoption with AWS.

In this blog post, we explore how to integrate NeMo 2.0 with SageMaker HyperPod to enable efficient training of large language models (LLMs). We cover the setup process and provide a step-by-step guide to running a NeMo job on a SageMaker HyperPod cluster.