Yves Pitsch

While most organizations are eager to adopt cloud technology, challenges like data sovereignty, regulatory compliance, and low-latency requirements are must-haves for some groups looking for a cloud provider. The need to keep sensitive data in certain locations, adhere to strict regulations, and ensure swift responsiveness can make it difficult to capitalize on the cloud's inherent advantages of innovation, scalability, and cost-efficiency. Designed with AI and data-intensive workloads in mind, Google Distributed Cloud offers a fully managed solution, a range of deployment options to fit your specific location needs, and the choice of running connected to Google Cloud's systems or air-gapped from the public internet. At Google Cloud Next, we announced a host of Google Distributed Cloud features and programs that enhance customers’ ability to adopt cutting-edge AI workloads across their environments. Read the latest Next ‘24 blog to learn more about the expanding GDC ecosystem of partner services and solutions.

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This is a major strategic announcement for both Google and Oracle and a huge market expansion opportunity for both. Most #enterprises continue to adopt a #multicloud strategy, powering #apps on the best fit solution for their needs. Google Cloud has been making major inroads into enterprises, and Oracle solutions and increasingly Oracle Cloud remains a strategic imperative for many organizations where their key data assets remain and where mission critical apps run. By enabling the Oracle Cloud Infrastructure suite to run on Google Cloud, and without charging any data transfer fees, Oracle is greatly expanding its potential market and scale, while Google gains an installed base to expand its presence against Amazon Web Services (AWS) and Microsoft Azure . This partnership also enables Oracle to add leading #AI capabilities to their solution through Google #Gemini which will continue to expand its capabilities over time and enable Oracle to not have to develop its own AI infrastructure, allowing it to concentrate on its enterprise grade business solutions. This is a win-win for both companies and a major opportunity for expansion as enterprises already operating on Google Cloud have a clear incentive to adopt and/or expand the use of Oracle capabilities without the need to implement another cloud instance. It's also a major opportunity for those companies that are still on-prem with Oracle solutions to have a #cloud upgrade path they may already be familiar with.

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Get ready for some serious fireworks because there's a game-changer brewing in the cloud. Oracle and Google Cloud are joining forces to unlock the EPIC potential of combining Oracle's best-in-class data and infrastructure with Google Cloud's revolutionary GenAI suite! This partnership gives you, the customer, the freedom to choose the best cloud solutions for your unique needs. And that's exactly what CloudWerx is all about – empowering businesses to thrive in the cloud. Talk about performance and flexibility! Way to go, Google Cloud and Oracle!

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Last week I did a presentation on "Cloud-Native Orchestration for Distributed Edge Intelligence” at the Data Innovation Summit in Stockholm, Sweden. I also manned a demo station in a booth with Lenovo Infrastructure Solutions Group and NVIDIA. Here are two reflections from talking about edge AI on the DIS show floor for two days. Most Nordic enterprises are still trying to wrap their heads around AI and what gains they can expect. A strong indicator of this was the high percentage of “AI consultants” roaming the show floor looking for new and interesting tools and potential customers. There seems to be an insatiable need for basic learning including guided exploration and trials. The basic sentiment was that much of the current investments in AI are defensive, think PoCs and platform trials. Organizations are currently building the competence and basic insights needed to make bigger bets down the line. A significant part of applied AI among industrial companies is moving to the edge or is being invented at the edge. I heard about numerous applications in computer vision, anomaly detection, and preventive maintenance that only work at the edge. Both because they consume enormous amounts of data that is too expensive to backhaul to the cloud, and because they need to work even in the face of upstream infrastructure issues. Both observations point toward putting investments into a platform-like infrastructure that provides a flexible and cost-effective way of experimenting with edge computing combined with AI and ensuring that the infrastructure is built with robustness and survivability at its core. Thank you Leif Nordlund for having me!

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What does Google Distributed Cloud (GDC) mean for our customers in the media and entertainment industry? ☁️🎬 GDC provides the ability for companies of all types to extend Google Cloud's infrastructure and AI services wherever they are needed - to data centers, on-premise locations, or at the edge. For the media and entertainment industry, here are 4 key areas GDC can now be used by studios and streamers alike: 1. Content Production & Post-Production - Media companies can now use GDC to process and render large media files locally, reducing the need to transfer data to the cloud, which can be time-consuming and expensive. This can speed up production workflows and enable real-time collaboration among creative teams in different locations around the world. 2. Content Delivery - GDC can be deployed at the edge, closer to end-users, to deliver content with lower latency and higher quality. This can improve the audience experience, especially for streaming services and live tentpole events. 3. Data Analytics and Machine Learning - Media companies can now use GDC to analyze large datasets and apply machine learning models to gain insights into audience behavior, content performance, and operational efficiency. This helps accelerate and improve decision-making about content creation, distribution, and monetization. 4. Security and Compliance - GDC can help media companies meet their security and compliance requirements by providing a secure environment for processing and storing sensitive data, such as subscriber information, content, and intellectual property. For more info on GDC, check out our recent blog post here https://lnkd.in/etAzvBKr #Google #GoogleCloud #Next24 #AI #GenAI #media #entertainment

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We are pleased to announce that you can now achieve up to an aggregate 1.3 million I/O operations per second (IOPS) and 12 GB/s throughput per OCI compute instance with the OCI Block Volume Service. In addition, you can also attach up to 32 Ultra High Performance (UHP) volumes to a single OCI compute instance. This service update is particularly useful for high I/O workloads, such as OLTP, AI/ML, data warehousing and analytics, databases with large data sets and extensive complex processing. https://lnkd.in/e3jGDyf4

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We do have a 45 TOPs NPU in Copilot+ PCs. But it’s not all about speed. You can get speed with GPUs too - even faster! But it’s about efficiency. It’s about battery life on your laptop that you are only allowed to fly (in the US) with a 100WHr battery capacity. Its about minimizing thermal throttling. Its about sustainability. When you get 45 TOPs with single digit watts, you can compute almost all the time. Something like Recall would totally trash your device experience when not offloaded to efficient hardware even if it is able to ‘run’. Phi Silica would take over your system resources without efficient hardware and if you want generative AI running in your games, you may soon end up thermally throttled.

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It's worthwhile to learn about how GPU-powered OCI instances can provide telco clients with faster performance, shorter time to insights, more efficiency, and higher worker productivity for manual-intensive jobs. 🤞

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"It used to take 8 to 12 weeks to get a report from request to production. With Incorta, business users can do that on their own, instantaneously” (Ajit Oak, Head of Business Intelligence at Broadcom). Here was Broadcom's challenge: data stored across multiple complex business systems made it difficult to analyze fast enough. The team relied on disparate software solutions, including Workday, Oracle ERP, Model N, Oracle Demantra, and Microsoft Excel. Despite investments in a traditional data warehouse and multiple data marts, generating a new analytical dashboard or report took 6-12 weeks. The delay between business requests and production was a critical pain point. Alan Davidson, Chief Information Officer at Broadcom, explained, “Our employees can’t wait weeks for the information they need to do their jobs. But at the same time, we have billions of rows of data in our database. It’s no small challenge to quickly slice and dice that amount of information.” ➡ Broadcom needed a solution that could: Reduce the time to deliver information to employees. AND Increase department productivity without draining internal resources. Other selection criteria included cost-effectiveness, self-service capability, ease of maintenance, minimal support from other technologies, and easy integration with external applications like Microsoft Excel. Here’s how Incorta solved Broadcom’s challenges: ➡ Instant Workflow With Incorta, IT no longer had to predict requests. We created flexible frameworks allowing business users to slice and dice data themselves. Request-to-production cycles that previously took 12 weeks were now achieved instantly. ➡ Increased Agility and Reduced TCO Incorta’s lightweight technology stack required less time and fewer people for maintenance and management, significantly reducing the total cost of ownership. ➡ Scalable for Growth Following several acquisitions, Broadcom needed to combine and analyze vast amounts of data from acquired organizations. Incorta enabled Broadcom to quickly create analytical applications with this new data, allowing employees to make faster, better-informed decisions during times of rapid change. ➡ Broadcom's Complete Migration to Incorta Cloud Broadcom migrated 100% of its data and infrastructure to the Incorta Cloud, adding more data, gaining faster loading times, and efficiently handling vast amounts of data. This move has significantly enhanced their data processing capabilities, ensuring quicker access to insights and better performance overall. Broadcom (AVGO) shares surged on Thursday as the semiconductor giant announced a 10-for-1 stock split. This strategic decision comes on the heels of a strong second-quarter earnings report, driven by high demand for the company's artificial intelligence products. Want the same data capabilities as Broadcom? You can. Book a 1:1 live demo of our open data delivery platform: https://lnkd.in/gK9hsKkm.

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To seamlessly implement AI in a product: Define clear objectives and use cases for AI integration. Gather relevant and clean data for training AI models. Select appropriate AI techniques and algorithms based on objectives. Develop and train AI models using suitable tools and frameworks. Seamlessly integrate AI models into product infrastructure. Design intuitive user interfaces with AI-powered features. Conduct rigorous testing and validation to ensure accuracy and performance. Implement monitoring tools for continuous AI performance evaluation. Ensure compliance with data privacy and ethical guidelines. Gather user feedback and iterate on AI implementation for improvement. Collaborate with AI experts and prioritize scalability, reliability, and value delivery.

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This piece dives deeper into one of our newest technologies, hollow core fiber (HCF). HCF technology was developed to meet the heavy demands of workloads like AI and improve global latency and connectivity. It uses a proprietary design where light propagates in an air core, which has significant advantages over traditional fiber built with a solid core of glass. An interesting piece here is that the HCF structure has nested tubes which help reduce any unwanted light leakage and keep the light going in a straight path through the core.   https://lnkd.in/e__yupWU

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Amazon FSx for NetApp ONTAP now supports #NVMe-over-#TCP for simpler, lower-latency shared block storage. Read more here...

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Data is the cornerstone of success for GenAI LLMs. Understanding language nuances, adapting to different contexts, and generating coherent responses hinges on the quality and quantity of the data trained on. In essence, data fuels the capabilities of GenAI LLMs, shaping their performance and ensuring relevance in the ever-evolving landscape of artificial intelligence. #GenAILLMs #DataDriven #AIInnovation

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Fascinating conversation.. The question is what percentage of future workloads will be running on HPC infrastructure vs customized CPU based server infrastructure.

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Business partnerships are often critical to success. How do you securely connect your suppliers, third-party contractors, ecosystem partners, etc., to your systems and services? Secure partner connectivity is a big use case for Alkira, Inc.

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