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This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to improve the performance of the model. …

Gradient makes LLM benchmarking cost-effective and effortless with AWS Inferentia Read More »

Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for generative AI, SageMaker Canvas …

Enable single sign-on access of Amazon SageMaker Canvas using AWS IAM Identity Center: Part 2 Read More »

Innovate, Connect, Cultivate  The Cloud Cultures series is an exploration of the intersection between cloud innovation and culture across the globe.  Last year, I visited Poland, Sweden, England, and Italy, and learned how the unique culture of each of these countries has shaped how they adopt and use technology, demonstrating the diverse ways in which …

Cloud Cultures, Part 5: Embracing innovation and preserving a vibrant identity in Mexico Read More »

This blog post is co-written with Hwalsuk Lee at Upstage. Today, we’re excited to announce that the Solar foundation model developed by Upstage is now available for customers using Amazon SageMaker JumpStart. Solar is a large language model (LLM) 100% pre-trained with Amazon SageMaker that outperforms and uses its compact size and powerful track records …

Solar models from Upstage are now available in Amazon SageMaker JumpStart Read More »

This is a guest post co-written with Meta’s PyTorch team and is a continuation of Part 1 of this series, where we demonstrate the performance and ease of running PyTorch 2.0 on AWS. Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. In …

Scale LLMs with PyTorch 2.0 FSDP on Amazon EKS – Part 2 Read More »

This is part of a larger series on the new infrastructure of the era of AI, highlighting emerging technology and trends in large-scale compute. This month, we’re sharing the 2024 edition of the State of AI Infrastructure report to help businesses harness the power of AI now.  The era of AI is upon us. You’ve …

New infrastructure for the era of AI: Emerging technology and trends in 2024 Read More »

As a young girl growing up, the tech world seemed like a distant reality, predominantly occupied by men. It wasn’t until my older sister pursued a degree in informatics in college that I realized the potential for women in technology. This revelation was not just about inspiring young girls like me but about fostering an …

Microsoft Azure AI celebrates Women’s History Month through our customers Read More »

Amazon Lex provides advanced conversational artificial intelligence (AI) capabilities to enable self-service support for your organization’s contact center. With Amazon Lex, you can implement an omnichannel strategy where customers engage via phone, websites, and messaging platforms. The bots can answer FAQs, provide self-service experiences, or triage customer requests before transferring to a human agent. Amazon Lex integrates …

Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center Read More »

Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These generative AI applications are not only …

Advanced RAG patterns on Amazon SageMaker Read More »

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good results for general tasks, such as …

Efficient continual pre-training LLMs for financial domains Read More »