5 ways AI will shape businesses in 2025
Learn more about Google Cloud’s AI predictions for businesses in 2025.
Llama 3.3 70B now available in Amazon SageMaker JumpStart
Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on …
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Whisk: Visualize and remix ideas using images and AI
Whisk is a new Google Labs experiment that lets you prompt using images for a fast and fun creative process.
Whisk: Visualize and remix ideas using images and AI
Whisk is a new Google Labs experiment that lets you prompt using images for a fast and fun creative process.
Whisk: Visualize and remix ideas using images and AI
Whisk is a new Google Labs experiment that lets you prompt using images for a fast and fun creative process.
AWS re:Invent 2024 Highlights: Top takeaways from Swami Sivasubramanian to help customers manage generative AI at scale
We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressions—and to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications. Q: What made this re:Invent different? Swami Sivasubramanian: The …
AI startups can apply for our Google for Startups Cloud AI Accelerator now
Learn more about Google for Startups Accelerator: AI First program for North American startups.
Multi-tenant RAG with Amazon Bedrock Knowledge Bases
Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using …
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How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows …
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Implementing login node load balancing in SageMaker HyperPod for enhanced multi-user experience
Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models (FMs) over extended periods. Multiple users — such as ML researchers, software engineers, data scientists, and cluster administrators — can work concurrently on the same cluster, each managing their own jobs and files without interfering …