Category: FAANG

This post is co-written with Lee Rehwinkel from Planview. Businesses today face numerous challenges in managing intricate projects and programs, deriving valuable insights from massive data volumes, and making timely decisions. These hurdles frequently lead to productivity bottlenecks for program managers and executives, hindering their ability to drive organizational success efficiently. Planview, a leading provider …

Read more “How Planview built a scalable AI Assistant for portfolio and project management using Amazon Bedrock”

The potential of AI has never been greater, and infrastructure plays a foundational role in driving it forward. AI Hypercomputer is our supercomputing architecture based on performance-optimized hardware, open software, and flexible consumption models. Together, these offer exceptional performance and efficiency, resiliency at scale, and give you the flexibility to choose offerings at each layer …

Read more “AI Hypercomputer software updates: Faster training and inference, a new resource hub, and more”

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. LLMs have the potential to revolutionize content creation and the way people use search engines and …

Read more “Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark”

This post is cowritten with Greg Benson, Aaron Kesler and David Dellsperger from SnapLogic. The landscape of enterprise application development is undergoing a seismic shift with the advent of generative AI. SnapLogic, a leader in generative integration and automation, has introduced the industry’s first low-code generative AI development platform, Agent Creator, designed to democratize AI …

Read more “Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock”

While LLM models deliver immense value for an increasing number of use cases, running LLM inference workloads can be costly. If you’re taking advantage of the latest open models and infrastructure, autoscaling can help you optimize your costs — ensuring you’re meeting customer demand while only paying for the AI accelerators you need. As a …

Read more “Save on GPUs: Smarter autoscaling for your GKE inferencing workloads”