News from the AI & ML world
DeeperML - #aws
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staff@insidehpc.com
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Amazon is making a substantial investment in artificial intelligence infrastructure, announcing plans to spend $10 billion in North Carolina. The investment will be used to build a cloud computing and AI campus just east of Charlotte, NC. This project is anticipated to create hundreds of good-paying jobs and provide a significant economic boost to Richmond County, positioning North Carolina as a hub for cutting-edge technology.
This investment underscores Amazon's commitment to driving innovation and advancing the future of cloud computing and AI technologies. The company plans to expand its AI data center infrastructure in North Carolina, following a trend among Big Tech companies who are building out infrastructure to meet escalating AI resource requirements. The new "innovation campus" will house data centers containing servers, storage drives, networking equipment, and other essential technology. Amazon is also focused on improving efficiency by enhancing warehouse operations through the use of AI. The company unveiled AI upgrades to boost warehouse efficiency. These upgrades center around the development of "agentic AI" robots. These robots are designed to perform a variety of tasks, from unloading trailers to retrieving repair parts and lifting heavy objects, all based on natural language instructions. The goal is to create systems that can understand and act on commands, transforming robots into multi-talented helpers, ultimately leading to faster deliveries and improved efficiency. Recommended read:
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Heng Chi@AI Accelerator Institute
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References:
LearnAI
, AI Accelerator Institute
AWS is becoming a standard for businesses looking to leverage AI and NLP through its comprehensive services. An article discusses how to design a high-performance data pipeline using core AWS services like Amazon S3, AWS Lambda, AWS Glue, and Amazon SageMaker. These pipelines are crucial for ingesting, processing, and outputting data for training, inference, and decision-making at a large scale, which is essential for modern AI and NLP applications that rely on data-driven insights and automation. The platform's scalability, flexibility, and cost-efficiency make it a preferred choice for building these pipelines.
AWS offers various advantages, including automatic scaling capabilities that ensure high performance regardless of data volume. Its flexibility and integration features allow seamless connections between services like Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift for data warehousing. Additionally, AWS’s pay-as-you-go pricing model provides cost-effectiveness, with Reserved Instances and Savings Plans enabling further cost optimization. The platform's reliability and global infrastructure offer a strong foundation for building effective machine learning solutions at every stage of the ML lifecycle. Generative AI applications, while appearing simple, require a more complex system involving workflows that invoke foundation models (FMs), tools, and APIs, using domain-specific data to ground responses. Organizations are adopting a unified approach to build applications where foundational building blocks are offered as services for developing generative AI applications. This approach facilitates centralized governance and operations, streamlining development, scaling generative AI development, mitigating risk, optimizing costs, and accelerating innovation. A well-established generative AI foundation includes offering a comprehensive set of components to support the end-to-end generative AI application lifecycle. Recommended read:
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@www.techmeme.com
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References:
Ken Yeung
, venturebeat.com
According to a new Amazon Web Services (AWS) report, generative AI has become the top IT priority for global organizations in 2025, surpassing traditional IT investments like security tools. The AWS Generative AI Adoption Index, which surveyed 3,739 senior IT decision makers across nine countries, reveals that 45% of organizations plan to prioritize generative AI spending. This shift signifies a major change in corporate technology strategies as businesses aim to capitalize on AI's transformative potential. While security remains a priority, the broad range of use cases for AI is driving the accelerated adoption and increased budget allocation.
The AWS study highlights several key challenges to GenAI adoption, including a lack of skilled workforce, the cost of development, biases and hallucinations, lack of compelling use cases, and lack of data. Specifically, 55% of respondents cited a lack of skilled workers as a significant barrier. Despite these challenges, organizations are moving quickly to implement GenAI, with 44% having moved beyond the proof-of-concept phase into production deployment. The average organization has approximately 45 GenAI projects or experiments in various stages, with about 20 of them transitioning into production. In response to the growing importance of AI, 60% of companies have already appointed a dedicated AI executive, such as a Chief AI Officer (CAIO), to manage the complexity of AI initiatives. This executive-level commitment demonstrates the increasing recognition of AI’s strategic importance within organizations. Furthermore, many organizations are creating training plans to upskill their workforce for GenAI, indicating a proactive approach to address the talent gap. The focus on generative AI reflects the belief that it can drive automation, enhance creativity, and improve decision-making across various industries. Recommended read:
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@www.techmeme.com
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A recent report from Amazon Web Services (AWS) indicates a significant shift in IT spending priorities for 2025. Generative AI has overtaken cybersecurity as the primary focus for global IT leaders, with 45% now prioritizing AI investments. This change underscores the increasing emphasis on implementing AI strategies and acquiring the necessary talent, even amidst ongoing skills shortages. The AWS Generative AI Adoption Index surveyed 3,739 senior IT decision makers across nine countries, including the United States, Brazil, Canada, France, Germany, India, Japan, South Korea, and the United Kingdom.
This move to prioritize generative AI doesn't suggest a neglect of security, according to Rahul Pathak, Vice President of Generative AI and AI/ML Go-to-Market at AWS. Pathak stated that customers' security remains a massive priority, and the surge in AI investment reflects the widespread recognition of AI's diverse applications and the pressing need to accelerate its adoption. The survey revealed that 90% of organizations are already deploying generative AI in some capacity, with 44% moving beyond experimental phases to production deployment, indicating a critical inflection point in AI adoption. The survey also highlights the emergence of new leadership roles within organizations to manage AI initiatives. Sixty percent of companies have already appointed a Chief AI Officer (CAIO) or equivalent, and an additional 26% plan to do so by 2026. This executive-level commitment reflects the growing strategic importance of AI, although the study cautions that nearly a quarter of organizations may still lack formal AI transformation strategies by 2026. These companies are planning ways to bridge the gen AI talent gap this year by creating training plans to upskill their workforce for GenAI. Recommended read:
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Michael Nuñez@AI News | VentureBeat
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References:
venturebeat.com
, www.marktechpost.com
Amazon Web Services (AWS) has announced significant advancements in its AI coding and Large Language Model (LLM) infrastructure. A key highlight is the introduction of SWE-PolyBench, a comprehensive multi-language benchmark designed to evaluate the performance of AI coding assistants. This benchmark addresses the limitations of existing evaluation frameworks by assessing AI agents across a diverse range of programming languages like Python, JavaScript, TypeScript, and Java, using real-world scenarios derived from over 2,000 curated coding challenges from GitHub issues. The aim is to provide researchers and developers with a more accurate understanding of how well these tools can navigate complex codebases and solve intricate programming tasks involving multiple files.
The latest Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4, further enhances LLM capabilities. This version supports a wider array of open-source models, including Meta’s Llama 4 models and Google’s Gemma 3, providing users with more flexibility in model selection. LMI v15 delivers significant performance improvements through an async mode and support for the vLLM V1 engine, resulting in higher throughput and reduced CPU overhead. This enables seamless deployment and serving of large language models at scale, with expanded API schema support and multimodal capabilities for vision-language models. AWS is also launching new Amazon EC2 Graviton4-based instances with NVMe SSD storage. These compute optimized (C8gd), general purpose (M8gd), and memory optimized (R8gd) instances offer up to 30% better compute performance and 40% higher performance for I/O intensive database workloads compared to Graviton3-based instances. They also include larger instance sizes with up to 3x more vCPUs, memory, and local storage. These instances are ideal for storage intensive Linux-based workloads including containerized and micro-services-based applications built using Amazon Elastic Kubernetes Service(Amazon EKS),Amazon Elastic Container Service(Amazon ECS),Amazon Elastic Container Registry(Amazon ECR), Kubernetes, and Docker, as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP. Recommended read:
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