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A Quantitative Comparison Of UPS Monitoring And Servicing Approaches Across Edge Environments

The white paper by EcoCare analyzes different UPS monitoring and servicing approaches for edge environments. It compares managing a fleet of UPSs internally vs outsourcing to a 3rd party. Key factors impacting costs include age distribution, downtime costs, and operational expenses. A tool helps quantify financial differences. Through scenarios, the paper shows outsourcing can provide significant savings, but internal management is better for newer assets. Read the full report to understand the tradeoffs and use the calculator tool to analyze your specific UPS fleet.

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  • How ISVs can accelerate Gen AI adoption with BigQuery

    New advancements in Gen AI can help ISVs unlock new revenue streams, personalized experiences, cost savings and more. To help you build a strong data foundation and accelerate your path to Gen AI adoption, Google Cloud brings the approach of organizing world’s information and making it universally accessible and useful to your organization. Google’s data and AI Cloud is built with and for AI, so that you can get to use the latest tools for training, tuning, and deploying your choice of AI models. Join this webinar to hear from the BigQuery Product Management team how the 1,000+ software partners whose solutions are built with BigQuery can leverage the latest AI/ML features to rapidly deliver GenAI experiences. You’ll learn: -How to apply Google’s foundational AI models to your data right inside BigQuery -How BigQuery Studio, which includes a new notebook experience, can accelerate data and AI workflows from data ingestion and preparation to analysis, exploration, and visualization — all the way to ML training and inference -How to generate and store text embeddings using SQL commands

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  • How ISVs can accelerate Gen AI adoption with BigQuery

    New advancements in Gen AI can help ISVs unlock new revenue streams, personalized experiences, cost savings and more. To help you build a strong data foundation and accelerate your path to Gen AI adoption, Google Cloud brings the approach of organizing world’s information and making it universally accessible and useful to your organization. Google’s data and AI Cloud is built with and for AI, so that you can get to use the latest tools for training, tuning, and deploying your choice of AI models. Join this webinar to hear from the BigQuery Product Management team how the 1,000+ software partners whose solutions are built with BigQuery can leverage the latest AI/ML features to rapidly deliver Gen AI experiences. You’ll learn: - How to apply Google’s foundational AI models to your data right inside BigQuery - How BigQuery Studio, which includes a new notebook experience, can accelerate data and AI workflows from data ingestion and preparation to analysis, exploration, and visualization — all the way to ML training and inference - How to generate and store text embeddings using SQL commands

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  • Advanced Analytics and Machine Learning with Data Virtualization

    Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture? Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data management platform. It can empower your organization, foster a data sharing culture, and simplify the life of the data scientist. Attend this webinar to learn: * How data virtualization simplifies the life of the data scientist, by overcoming data access and manipulation hurdles. * How integrated Denodo Data Science notebook provides for a unified environment * How Denodo uses AI/ML internally to drive the value of the data and expose insights * How customers have used Data Virtualization in their Data Science initiatives.

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  • Denodo’s Data Catalog: Accelerate AI/ML Modeling with Faster Access to Datasets

    Data Scientists spend most of their time looking for the right data and massaging it into a usable format. How can the Denodo platform enable the data scientist and data engineer? Join us as we explore and demonstrate how the Denodo 8.0 Data Catalog can help the data scientist and data engineers to find the relevant datasets quickly by using any metadata information and also using the respective categories and tags. Explore how the data catalog provides more information on the dataset allowing to define the descriptions for the views and fields, showing the associations with other datasets and also the lineage of the datasets. Discover how the Denodo data catalog enables Data Scientists to obtain the data using JDBC/ODBC methods from popular languages like Python/R with their tools of choice such as Zeppelin or Jupyter. Attend this webinar to learn: ● How to explore datasets available using Denodo Data Catalog ●How to categorize datasets in Denodo Data Catalog ●How to find the relationships between datasets, and the lineage in a business friendly way ● How to connect to Denodo using popular notebooks ● How customers have used Data Virtualization in their Data Science initiatives

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  • Build Next-Gen, Portable, Power-Efficient AI on an AI PC

    What is an AI PC and how do developers exploit its AI acceleration capabilities across its included CPU, GPU, and NPU? This session delivers answers and unpacks fundamental and advanced techniques for tapping into the ever-expanding potential of AI using the OpenVINO™ toolkit on the AI PC. Watch a discussion on: An overview of an AI PC. Approaches for making current-state and next-generation AI and generative AI (GenAI) models more performant and power efficient. Reasons to consider running AI and GenAI models on client and edge devices and tips on low-power implementations. Techniques for optimizing and deploying AI applications on different AI PC compute engines using the OpenVINO toolkit. How to access and use the OpenVINO™ notebook repository with an overview of its functionalities and applications. Demos showcase how to seamlessly transition AI and GenAI apps across compute engines using OpenVINO toolkit for popular use cases, such as background blurring on video calls, object detection, and GenAI-powered image generation and chatbots. Skill level: All

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  • Why and what is the future of the topological qubit?

    Join us to explore why Microsoft decided to design its quantum machine with topological qubits – an approach that is both more challenging and more promising than others – and what’s next for Microsoft’s hardware ambitions. This episode is based on Microsoft’s physics breakthrough outlined in Dr. Nayak’s recent paper and will focus on the physics behind the topological qubit. Learn about topological phases in physics and how they are applied to quantum computing. Explore how topological properties create a level of protection that can, in principle, help a qubit retain quantum information despite what’s happening in the environment around it. Understand the role of the topological gap and the recently discovered Majorana zero modes, and how together they impact a topological qubit’s stability, size, and speed. Learn how to examine the raw data and analysis from Microsoft’s hardware research on Azure Quantum. Use interactive Jupyter notebooks and explore what’s next in engineering the world’s first topological qubit. Participate in live Q&A chat with the Azure Quantum Team and be one of the first to hear about recent advancements.

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  • Arquitectura de Data Fabric: Clave en proyectos de Big Data y Machine Learning

    Los proyectos de Big Data y Machine Learning en muchos casos no logran los beneficios esperados y los lagos de datos se convierten en nuevos silos de datos que aportan poco valor a negocio. Las arquitecturas de Data Fabric vienen a facilitar el ciclo de vida de las iniciativas de Big Data y Machine Learning: • Exploración e identificación de datos relevantes para el análisis mediante el Catálogo de Datos • Preparación de los datos para alimentar los algoritmos de ML (con total trazabilidad de las combinaciones y transformaciones realizadas) • Parametrización del algoritmo, tuning y adiestramiento mediante data science notebooks conectados a la capa de virtualización • Operacionalización del algoritmo como un servicio de datos para usuarios de negocio • Ofrecen una capa de gobierno y seguridad sobre las fuentes de datos

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