Tag: ML

Boost ASIC verification efficiency with SilverLining EDA’s VeriFire on Amazon EC2 F2 Instances: Unlock up to 60% better price-performance compared to F1, and accelerate your FPGA build times by 80% with SilverLining EDA’s cloud-based emulation solution.

Subsurface infrastructure mapping is crucial for industries ranging from oil and gas to environmental protection. Our groundbreaking approach combines advanced magnetic imaging with physics-informed AI to provide unparalleled visibility into hidden structures, even when traditional methods fall short. Explore how this fusion of cloud computing and AI is opening new possibilities for subsurface exploration and management.

In this post, we’ll examine how this multi-agent approach works, the architecture behind it, and the efficiency improvements it enables. While the focus is on an engineering use case, the principles apply broadly to any organization facing the challenge of coordinating specialized expertise to deliver faster, more consistent results.

Capacity Blocks for ML (CBML) are a powerful feature that allows you to reserve highly sought-after GPU based EC2 instances for a future date to support your short-duration machine learning (ML) workloads. Since the reservations are “for a future date” you must have a mechanism to launch the instances that you have paid for and place jobs onto them at that specific time. This is where AWS Batch comes in. With an always-on queue ready to accept jobs, and the ability to scale your capacity block reservation at the correct time, AWS Batch provides you with everything you need to maximize your CBML reservations.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 2 covers implementation details and results.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 1 of 2 covers the theoretical foundations of the approach.

AWS Parallel Computing Service now supports AWS CloudFormation, enabling you to deploy and scale HPC workloads as code. Check out our open-source HPC Recipes Library for quick cluster deployments.

Curious about running AI-powered protein folding analyses on AWS? Quantori’s new solution makes it easy to test generative models and visualize results in your own cloud environment.

LLMs are scaling exponentially. Learn how advanced technologies like Triton, TRT-LLM and EKS enable seamless deployment of models like the 405B parameter Llama 3.1. Let’s go large.

Combining generative AI with AWS services, Nifco USA is exploring new frontiers in structural design. See how they’re using diffusion models, SageMaker, and Batch to create game-changing lightweight auto parts.