Today, we’re announcing the general availability of Amazon SageMaker HyperPod recipes to help data scientists and developers of all skill sets to get started training and fine-tuning foundation models (FMs) in minutes with state-of-the-art performance. They can now access optimized recipes for training and fine-tuning popular publicly available FMs such as Llama 3.1 405B, Llama 3.2 90B, or Mixtral 8x22B. At AWS re:Invent 2023, we introduced SageMaker HyperPod to reduce time to train FMs by up to 40 percent and scale across more than a thousand compute resources in parallel with preconfigured distributed training libraries. With SageMaker HyperPod, you can find the required accelerated compute resources for training, create the most optimal training plans, and run training workloads across different…
AWS Education Equity Initiative: Applying generative AI to educate the next wave of innovators
Building on the work that we and our partners have been doing for many years, Amazon is committing up to $100 million in cloud technology and technical resources to help existing, dedicated learning organizations reach more learners by creating new and innovative digital learning solutions, all as part of the AWS Education Equity Initiative. The Work So Far AWS and Amazon have a long-standing commitment to learning and education. Here’s a sampling of what we have already done: AWS AI & ML Scholarship Program – This program has awarded $28 million in scholarships to approximately 6000 students. Machine Learning University – MLU offers a free program helping community colleges and Historically Black Colleges and Universities (HBCUs) teach data management, artificial…
Solve complex problems with new scenario analysis capability in Amazon Q in QuickSight
Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply…
Use Amazon Q Developer to build ML models in Amazon SageMaker Canvas
As a data scientist, I’ve experienced firsthand the challenges of making machine learning (ML) accessible to business analysts, marketing analysts, data analysts, and data engineers who are experts in their domains without ML experience. That’s why I’m particularly excited about today’s Amazon Web Services (AWS) announcement that Amazon Q Developer is now available in Amazon SageMaker Canvas. What catches my attention is how Amazon Q Developer helps connect ML expertise with business needs, making ML more accessible across organizations. Amazon Q Developer helps domain experts build accurate, production-quality ML models through natural language interactions, even if they don’t have ML expertise. Amazon Q Developer guides these users by breaking down their business problems and analyzing their data to recommend step-by-step…