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Someone impossible to buy for? Perhaps AI can help
2024-12-06 01:02:06| BBC News | Business | UK Edition
With vast amounts of data do draw on, AI should be able to come up with good gift ideas, but does it?
Category: Consumer Goods and Services
New photos released as NYPD hunt insurance boss killer
2024-12-05 23:04:35| BBC News | Business | UK Edition
Police are still working to determine a motive in the slaying of insurance executive Brian Thompson.
Category: Consumer Goods and Services
Boeing plea deal tied to fatal crashes rejected
2024-12-05 20:06:29| BBC News | Business | UK Edition
The decision raises new questions for the embattled aerospace giant.
Category: Consumer Goods and Services
Telecommunications
Key Highlights from AWS re:Invent 2024: Dr. Swami Sivasubramanians Vision for Gen AI
2024-12-05 18:11:54| The Webmail Blog
Key Highlights from AWS re:Invent 2024: Dr. Swami Sivasubramanians Vision for Gen AI juli0507 Thu, 12/05/2024 - 11:11 Key Highlights from AWS re:Invent 2024: Dr. Swami Sivasubramanians Vision for Gen AI December 5, 2024 by Paul Jeyasingh, Head of Presales (US), Data Analytics and Gen AI, Rackspace Technology Dr. Swami Sivasubramanians keynote was one of the most anticipated sessions at AWS re:Invent 2024, drawing thousands of ML and generative AI enthusiasts. In his address, Sivasubramanian unveiled a host of new features and updates designed to accelerate the generative AI journey. Central to this effort is Amazon SageMaker, which simplifies the machine learning (ML) lifecycle by integrating data preparation, model training, deployment and observability into a unified platform. Over the past year, SageMaker has introduced over 140 new capabilities to enhance ML workflows, and Sivasubramanian highlighted groundbreaking updates to HyperPod and the ability to deploy partner AI apps seamlessly within SageMaker. HyperPod plans simplify LLM training Companies that are building their own LLMs need massive infrastructure capacity. Procuring this infrastructure and reserving hardware at such scale takes considerable time. Thats why we love HyperPod training plans theyre a game-changer for streamlining the model training process. These plans enable teams to quickly create a training plan that automatically reserves the required capacity. HyperPod sets up a cluster, initiates model training jobs and can save data science teams weeks in the training process. Built on EC2 capacity blocks, HyperPod creates optimal training plans tailored to specific timelines and budgets. HyperPod also provides individual time slices and available AZs to accelerate model readiness through efficient checkpointing and resuming. It automatically handles instance interruptions, allowing training to continue seamlessly without manual intervention. HyperPod task governance improves resource efficiency HyperPod task governance helps companies maximize compute resource utilization such as accelerators by automating the prioritization and management of model training, fine-tuning and inference tasks. With task governance, companies can set resource limits by team or project while monitoring utilization to ensure efficiency. This capability can help reduce infrastructure costs, potentially by up to 40%, according to AWS. Partner AI apps enhance SageMakers capabilities One of the standout updates shared during the keynote was the ability to deploy partner AI applications directly within Amazon SageMaker. This new feature streamlines the model deployment lifecycle, providing a fully managed experience with no infrastructure to provision or operate. It also leverages SageMakers robust security and privacy features. Among the available applications are Comet, Deepchecks, Fiddler and Lakera, each offering unique value to accelerate machine learning workflows. Amazon Nova LLMs bring versatility to Bedrock During his keynote, Sivasubramanian introduced Amazon Nova, a groundbreaking family of large language models (LLMs) designed to expand the capabilities of Amazon Bedrock. Each model is tailored to specific generative AI use cases, with highlights including: Amazon Nova Micro: A text-only model optimized for ultra-low-latency responses at minimal cost Amazon Nova Lite: A multimodal model delivering low-latency processing for image, video, and text inputs at a very low cost Amazon Nova Pro: A versatile multimodal model balancing accuracy, speed, and cost for diverse tasks Amazon Nova Premier: The most advanced model, built for complex reasoning and serving as the best teacher for distilling custom models (available Q1 2025) Amazon Nova Canvas: A cutting-edge model specialized in image generation Amazon Nova Reel: A state-of-the-art model for video generation These Nova models reflect AWS's commitment to addressing the diverse needs of developers and enterprises, delivering tools that combine cost-efficiency with advanced capabilities to fuel innovation across industries. Poolside Assistant expands software development workflows Another standout announcement from the keynote was AWSs collaboration with Poolside Assistant, a startup specializing in software development workflows. Powered by Malibu and Point models, it excels at tasks like code generation, testing and documentation. AWS is the first cloud provider to offer access to this assistant, expected to launch soon. Stability.ai Stable Diffusion 3.5 advances text-to-image generation Stability.ais Stable Diffusion 3.5 model, trained on Amazon SageMaker HyperPod, is coming soon to Amazon Bedrock. This advanced text-to-image model, the most powerful in the Stable Diffusion family, opens new possibilities for creative and technical applications. Luma AI introduces high-quality video generation with RAY2 Luma AIs RAY2 model, arriving soon in Amazon Bedrock, enables high-quality video generation with support for text-to-video, image-to-video and video-to-video capabilities. Amazon Bedrock Marketplace simplifies model discovery The Amazon Bedrock Marketplace offers a single catalog of over 100 foundation models, enabling developers to discover, test and deploy models on managed endpoints. Integrated tools like Agents and Guardrails make it easier to build and manage AI applications. Amazon Bedrock Model Distillation enhances efficiency Model Distillation in Amazon Bedrock simplifies the transfer of knowledge from large, accurate models to smaller, more efficient ones. These distilled models are up to 500% faster and 75% less expensive than their original counterparts, with less than 2% accuracy loss for tasks like Retrieval-Augmented Generation (RAG). This feature allows businesses to deploy cost-effective models without sacrificing use-case-specific accuracy. Amazon Bedrock Latency Optimized Inference accelerates responsiveness Latency Optimized Inference significantly improves response times for AI applications without compromising accuracy. This enhancement requires no additional setup or fine-tuning, enabling businesses to immediately boost application responsiveness. Amazon Bedrock Intelligent Prompt Routing optimizes AI performance Intelligent Prompt Routing selects the best foundation model from the same family for each request, balancing quality and cost. This capability is ideal for applications like customer service, routing simple querie to faster, cost-effective models and complex ones to more capable models. By tailoring model selection, businesses can reduce costs by up to 30% without compromising accuracy. Amazon Bedrock introduces prompt caching A standout feature announced during the keynote was prompt caching in Amazon Bedrock, which allows frequently used context to be retained across multiple model invocations for up to five minutes. This is especially useful for document Q&A systems or coding assistants that need consistent context retention. Prompt caching can reduce costs by up to 90% and latency by up to 85% for supported models. Amazon Kendra Generative AI Index enhances data retrieval The new Amazon Kendra Generative AI Index provides a managed retriever for Retrieval-Augmented Generation (RAG) and Bedrock, with connectors to 43 enterprise data sources. This feature integrates with Bedrock knowledge bases, enabling users to build generative AI-powered assistance with agents, prompt flows and guardrails. Its also compatible with Amazon Q business applications. Structured data retrieval in Bedrock Knowledge Bases One of the most requested features, structured data retrieval, is now available in Bedrock Knowledge Bases. Users can query data in Amazon Redshift, SageMaker Lakehouse and S3 tables with Iceberg support using natural language. The system transforms these queries into SQL, retrieving data directly without preprocessing. GraphRAG links relationships in knowledge bases Bedrock Knowledge Bases now support GraphRAG, combining RAG techniques with Knowledge Graphs to enhance generative AI applications. This addition improves accuracy and provides more comprehensive responses by linking relationships across data sources. Amazon Bedrock Data Automation streamlines workflows Amazon Bedrock Data Automation enables the quick creation of workflows for intelligent document processing (IDP), media analysis and RAG. This feature can extract and analyze multimodal data, offering insights like video summaries, detection of inappropriate image content and automated document analysis. Multimodal data processing in Bedrock Knowledge Bases To support applications handling both text and visual data, Bedrock Knowledge Bases now process multimodal data. Users can configure the system to parse documents using Bedrock Data Automation or a foundation model. This improves the accuracy and relevancy of responses by incorporating information from text and images. Guardrails expand to multimodal toxicity detection Another exciting update is multimodal toxicity detection in Bedrock Guardrails. This feature extends safeguards to image data, and should help companies build more secure generative AI applications. It prevents interaction with toxic content, including hate, violence and misconduct, and is available for all Bedrock models that support image data. Harnessing these innovations in the future The keynote by Dr. Swami Sivasubramanian showcased numerous groundbreaking announcements that promise to transform the generative AI and machine learning landscape. While weve highlighted some of the most exciting updates, theres much more to explore. These innovations offer incredible potential to help businesses deliver impactful outcomes, create new revenue opportunities and achieve cost savings at scale. At Rackspace Technology, were excited to help organizations harness these advancements to optimize their data, AI, ML and generative AI strategies. Visit our Amazon Marketplace profile to learn more about how we can help you unlock the future of cloud computing and AI. For additional insights, view this webinar, Building the Foundation for Generative AI with Governance and LLMOps, which looks more closely at governance strategies and operational excellence for generative AI. Recent Posts Key Highlights from AWS re:Invent 2024: Dr. Swami Sivasubramanians Vision for Gen AI December 5th, 2024 Key Highlights from AWS re:Invent 2024: CEO Keynote with Matt Garman December 4th, 2024 Highlights from Monday Night Live: Embracing the How of AWS Innovations December 4th, 2024 UK Financial Services Prepare for January 2025 DORA Implementation November 1st, 2024 Dispelling Myths About Running OpenStack Clouds August 19th, 2024 Links Blog Home Solve: Thought Leadership Newsroom Investor Relations Media Kit
Category: Telecommunications
Key Highlights from AWS re:Invent 2024: CEO Keynote with Matt Garman
2024-12-04 20:51:11| The Webmail Blog
Key Highlights from AWS re:Invent 2024: CEO Keynote with Matt Garman juli0507 Wed, 12/04/2024 - 13:51 Key Highlights from AWS re:Invent 2024: CEO Keynote with Matt Garman December 4, 2024 by Gary Porter, Senior Solution Architect, Professional Services, Rackspace Technology Watching Matt Garman take the stage at AWS re:Invent 2024 for his inaugural keynote as AWS CEO, I was struck by the significance of the moment. Addressing more than 60,000 attendees in Las Vegas and 400,000 online, Garman shared an ambitious vision for empowering customers spanning foundational services like compute and storage to groundbreaking advancements in AI. His keynote underscored AWSs mission to equip organizations with the tools they need to solve complex challenges and scale operations while shaping a brighter future. At the heart of this mission lies a steadfast commitment to innovation and transformation. We invent so you can reinvent Garman reflected on AWSs origins and its enduring commitment to innovation. He highlighted the companys unique approach of working backward from customer needs a strategy that has consistently delivered impactful solutions. Garman also announced a $1 billion global fund for startups launching in 2025, reaffirming AWSs dedication to fostering innovation and empowering the next generation of builders. Building blocks for the future with compute, storage and databases AWSs leadership in compute and security took center stage, with Garman describing these areas as the foundation for driving innovation. AWS continues to push the boundaries of compute with its custom-built processors. The fourth-generation Graviton processor delivers 45% better performance on Java workloads while reducing energy consumption by 60%. For example, Pinterests transition to Graviton resulted in a 47% workload reduction and a 62% decrease in carbon emissions. Garman emphasized that security is built-in at every layer, helping AWS services remain secure by design. The next frontier, according to Garman, is generative AI workloads. Most of these workloads rely on GPUs, and AWSs collaboration with NVIDIA has resulted in the P6 instances featuring Blackwell chips, which are 2.5 times faster than previous generations. AWSs own Trainium processors for AI training continue to advance, with the introduction of Tranium 2 UltraServers (Trn2), designed to expand what large AI models can achieve within a single massive instance. Garman also announced the next-generation Trainium 3 processor, slated for release in 2025, further strengthening AWSs position as a leader in AI infrastructure. Shifting gears to storage and databases Following his discussion on compute, Garman turned his attention to AWSs storage and database services. Garman highlighted how AWS S3, now storing over 400 trillion objects, continues to evolve with features like Intelligent Tiering, which has saved customers over $4 billion. But as storage grows, so does its complexity. To address the increasing use of tabular data formats like Apache Parquet, Garman introduced Amazon S3 Tables, enabling the incorporation of multiple data types into S3 using Apache Iceberg for indexing. Metadata management is also getting an upgrade with the announcement of the Amazon S3 Metadata service, now available in preview, which automates metadata processes to further simplify data management. Amazon Aurora, now celebrating 10 years as one of AWSs fastest-growing services, continues to innovate at a rapid pace. Tackling the challenge of multi-region consistency, Garman explained how AWS revisited database fundamentals. By leveraging advancements in the Amazon Time Sync Service and streamlining the transaction commit process, Aurora has reduced time-to-consistency from seconds to microseconds. These advancements are now previewed in the new Amazon Aurora DSQL service, which is designed to enable low-latency, multi-region consistency for modern applications. Garman also noted that the same approach has been applied to NoSQL databases, enhancing Amazon DynamoDB Global Tables for faster and more consistent performance. AI at the core of tomorrows applications Generative AI is transforming industries, and during his keynote, Garman illustrated its potential with several compelling examples. Amazon Bedrock, AWSs foundational platform for deploying and managing generative AI models, is at the center of this transformation. Customers like Genentech leverage Amazon Bedrock for complex applications such as drug discovery reducing processes that once took years to mere minutes. One of the latest advancements is Amazon Bedrock Model Distillation, which simplifies AI models and tunes them for specific use cases. Additionally, Amazon Bedrock Guardrails ensure accuracy by addressing concerns over hallucinations in generative AI outputs. Expanding capabilities further, Amazon Bedrock Agents enable multi-agent collaboration and workflows, allowing customers to automate complex tasks using natural language instructions. Moodys used these tools during beta testing and found substantial time savings in its analytics workflows. AWS also introduced Amazon Nova, a suite of multi-modal AI models optimized for diverse applications. Amazon Nova Lite benchmarks on par with or better than popular models like Llama and Gemini, while Amazon Nova Canvas and Amazon Nova Reel support image and video generation. Garman teased even more ambitious advancements, including multi-modal-to-multi-modal models expected to launch in mid-2025. Finally, Amazon Q, which is AWSs AI-powered development and operations platform, took the spotlight for its ability to accelerate application modernization. By automating tasks such as unit testing, documentation and code reviews, Amazon Q reduces time-to-code and simplifies legacy platform migrations, enabling seamless upgrdes for Windows and VMware applications. Simplifying data and AI workflows Simplifying workflows and unifying data access were central themes in Garmans keynote. With Amazon Q Business, AWS offers businesses a way to consolidate data across formats and leverage powerful indexing capabilities. These features enable organizations to streamline workflows, automate processes and maintain robust security. Garman also highlighted advancements in Amazon SageMaker, a central platform for data and analytics in AI model training. Recognizing the complexity of navigating across multiple screens, AWS introduced Amazon SageMaker Unified Studio a centralized interface where users can configure all AI-related activities in one place. To further simplify data access, AWS also unveiled Amazon SageMaker Lakehouse, which enhances the data layer by unifying access to data across S3, Redshift, SaaS and federated sources. Bringing innovation to life through customer success Over the course of Garmans address, AWS's ability to innovate came to life through compelling customer success stories. Pinterests adoption of Graviton processors led to significant cost savings and a 62% reduction in carbon emissions, while JP Morgan Chase demonstrated AWSs capability to handle complex, high-performance workloads. Genentech showcased how Amazon Bedrock accelerates drug development, reducing the length of processes that once took years to mere minutes. Moodys highlighted the power of Amazon Bedrock Agents, which helped automate advanced analytics workflows and achieve substantial efficiency gains. Garman concluded his keynote by reaffirming AWSs commitment to providing customers with choice and control over their technology. From compute to AI and beyond, AWS is helping customers achieve their business goals while driving innovation across the cloud landscape. At Rackspace Technology, were proud to partner with AWS to deliver these innovations to our customers. Visit our Amazon Marketplace profile to explore AWS services available to your organization. Recent Posts Key Highlights from AWS re:Invent 2024: CEO Keynote with Matt Garman December 4th, 2024 Highlights from Monday Night Live: Embracing the How of AWS Innovations December 4th, 2024 UK Financial Services Prepare for January 2025 DORA Implementation November 1st, 2024 Dispelling Myths About Running OpenStack Clouds August 19th, 2024 Rackspace Technology Powers Hands-on Labs (HOL) at VMware Explore 2024 August 9th, 2024 Links Blog Home Solve: Thought Leadership Newsroom Investor Relations Media Kit
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Highlights from Monday Night Live: Embracing the How of AWS Innovations
2024-12-04 15:10:22| The Webmail Blog
Highlights from Monday Night Live: Embracing the How of AWS Innovations juli0507 Wed, 12/04/2024 - 08:10 Highlights from Monday Night Live: Embracing the How of AWS Innovations December 4, 2024 by Jon (JR) Price, Sr. Manager, Rackspace Elastic Engineering, Rackspace Technology When Peter DeSantis, SVP of Utility Computing at AWS, took the stage at Monday Night Live during AWS re:Invent 2024, the room buzzed with anticipation. Known for delivering thought-provoking insights, DeSantiss keynotes are always a highlight of the event, and this year was no exception. His address provided a detailed look at the innovations driving AWSs progress and their focus on understanding the how behind their technology. DeSantis compared this philosophy to the deep taproot of a tree, drawing water from hidden underground sources a metaphor for AWS leaders immersing themselves in technical details. This focus enables them to make informed decisions, anticipate customer needs and address challenges proactively. Another key theme DeSantis explored was the collaborative culture within AWS. He described how teams work together across all layers of the technology stack from data center power and networking to custom chips and software much like the interconnected root systems of the Amazon rainforest. This synergy enables AWS to develop integrated solutions that address a wide range of customer challenges while supporting continuous innovation. By fostering such cross-functional collaboration, AWS is able to refine its offerings and adapt to the changing needs of its customers. One example of this collaborative innovation is AWSs work on custom silicon, showcased through the evolution of their Graviton processors. The Graviton custom silicon journey During the keynote, DeSantis highlighted the evolution of AWSs custom silicon development, which has been central to their strategy for optimizing cloud performance: Graviton (2018): Introduced to promote industry collaboration around ARM in data centers, providing developers with tangible hardware to test Graviton2: AWSs first purpose-built processor, designed for scaled-out workloads such as web servers, microservices and caching fleets Graviton3: Delivered significant performance improvements, targeting specialized workloads requiring high compute power, including machine learning inference, scientific modeling and video transcoding Graviton4: AWSs most advanced chip to date, featuring multi-socket support and triple the original vCPU count, making it suitable for demanding enterprise workloads such as large databases and complex analytics Rather than focusing on synthetic benchmarks, AWS evaluates real-world performance to better align their processors with customer needs. For example, while Graviton3 demonstrated a 30% improvement over Graviton2 in traditional benchmarks, real-world applications like NGINX saw up to a 60% performance increase. This emphasis on practical performance has contributed to the growing adoption of Graviton processors, which now account for more than 50% of all new CPU capacity in AWS data centers. By optimizing their silicon for real-world workloads, AWS has built a compelling case for customers seeking reliable and scalable cloud infrastructure. Revolutionizing security with the AWS Nitro System Security remains a top priority in the cloud, and the AWS Nitro System represents a significant evolution in how infrastructure can be built and secured. Nitros hardware-based security begins at manufacturing, where it provides cryptographic proof known as attestation to verify what is running on each system. This creates an unbroken chain of custody and verification, ensuring the integrity of components from the moment they are manufactured to when they are in operation. With Graviton4, AWS extended attestation to the processor itself, establishing an interconnected framework of trust between critical system components. Connections such as CPU-to-CPU communication and PCIe traffic are protected through hardware-based security anchored in the manufacturing process. This design addresses challenges inherent to traditional servers and data centers, enabling enhanced protection and operational confidence. Introducing disaggregated storage with Nitro AWS identified a growing challenge with traditional storage servers: As hard drive capacities increased, these systems struggled to keep pace due to fixed compute-to-storage ratios and tightly coupled components. Scaling up storage servers by simply adding more capacity became inefficient and operationally complex. Recognizing these limitations, AWS took a different approach by breaking storage systems down into more manageable and scalable components. AWSs disaggregated storage leverages Nitro technology by embedding Nitro cards directly into JBOD (Just a Bunch of Disks) enclosures. This design gives each drive its own intelligence and network connectivity, eliminating the constraints of traditional fixed ratios. Independent scaling becomes possible, enabling flexible resource allocation based on actual needs. Failures are isolated to individual components, reducing their overall impact and accelerating recovery times. Maintenance is simplified and capacity planning becomes more manageable. As hard drive capacities continue to expand, this disaggregated approach ensures storage solutions can scale effectively into the future. Advancing AI infrastructure with Tranium2 AI workloads, particularly in model training and inference, present unique challenges. These tasks often require a scale-up approach rather than scale-out due to limitations such as global batch size in data parallelism. To meet these challenges, AWS developed Tranium2, a next-generation AI training chip that incorporates advanced features to optimize performance for demanding workloads: Systolic array architecture: Unlike traditional CPUs and GPUs, Tranium2 uses a systolic array designed specifically for AI workloads, optimizing memory bandwidth and computational efficiency Advanced packaging techniques: High-bandwidth memory (HBM) modules are integrated using interposers, enabling efficient use of space and maximizing chip performance within manufacturing constraints. Innovations in power delivery: By positioning voltage regulators closer to the chip, AWS reduced voltage drop issues, improving performance and chip longevity. Automated manufacturing: The Tranium2 chip is optimized for rapid scaling and deployment, ensuring customers can access the technology quickly and seamlessly. The Tranium2 server is engineered to handle demanding workloads, offering 20 petaflops of compute capacity and 1.5 terabytes of high-speed HBM memory. AWSs proprietary interconnect technology, NeuronLink, enables multiple Tranium2 servers to function as a single logical unit.These ultra servers are essential for training next-generation AI models with trillions of parameters, pushing the boundaries of whats possible in AI infrastructure. Enhancing AI inference with Amazon Bedrock Recognizing the critical role of both training and inference in AI workloads, AWS introduced latency-optimized options for Amazon Bedrock. These enhancements provide customers with access to the latest AI hardware and software optimizations to enable faster and more efficient inference times. Through partnerships with leading AI models such as Metas Llama 2 and Anthropics Claude 3.5, AWS continues to enhance performance for diverse AI use cases. For example, latency-optimized versions of Llama 2 70B and 34B now deliver some of the fastest inference speeds available on AWS. Similarly, a latency-optimized version of Claude 3.5, developed in collaboration with Anthropic, achieves a 60% improvement in speed over the standard model. Collaborating with Anthropic: Project Rainier Tom Brown, co-founder and Chief Compute Officer at Anthropic, provided insights into Project Rainier a high-performance AI cluster powered by hundreds of thousands of Tranium2 chips. This cluster delivers over five times the compute power of previous generations, enabling faster development of the next generation of Claude, Anthropics AI assistant. With this enhanced infrastructure, customers will gain access to smarter AI agents that operate at lower costs and faster speeds, enabling them to tackle larger and more complex projects. This collaboration exemplifies how AWS is partnering with industry leaders to push the boundaries of AI infrastructure. Scaling AI clusters with elastic AI-optimized networking AWS showcased its latest generation AI network fabric, the 10P10U Network, designed to deliver massive capacity and ultra-low latency. This advanced fabric provides tens of petabits of network capacity to thousands of servers, achieving latency under 10 microseconds. It can scale flexibly, from a few racks to clusters spanning multiple data center campuses. To simplify deployment, AWS introduced proprietary trunk connectors, which reduce installation time by 54% and virtually eliminate connection errors. Another key innovation is Scalable Intent-Driven Routing (SIDR), a new network routing protocol that combines central planning with decentralized execution. This approach enables quick, autonomous responses to failures, improving both reliability and performance. Driving innovation across the entire stack I left Monday Night Live with a sense of how AWSs focused approach to innovation is driving the future of cloud computing and AI infrastructure. As DeSantis emphasized, their unique culture and horizontal integration enable advancements across data centers, networking, custom silicon, and softwaremeeting the demands of modern workloads and shaping whats next. Discover how Rackspace Technology can help you harness these innovations to transform your cloud strategy. Visit our Amazon Marketplace profile to explore our solutions, and together, we can shape the future of cloud computing and AI. Recent Posts Highlights from Monday Night Live: Embracing the How of AWS Innovations December 4th, 2024 UK Financial Services Prepare for January 2025 DORA Implementation November 1st, 2024 Dispelling Myths About Running OpenStack Clouds August 19th, 2024 Rackspace Technology Powers Hands-on Labs (HOL) at VMware Explore 2024 August 9th, 2024 Why You Need Proactive Modern Operations in a Complex IT World August 7th, 2024 Links Blog Home Solve: Thought Leadership Newsroom Investor Relations Media Kit
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