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了解最新的云产品特性和使用案例,包括云计算、云服务等,帮助您更好地了解云产品的发展和应用。

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浅时光博客

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亚马逊AWS官方博客

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AWS 一周综述:Amazon EC2 G6 实例、Amazon Bedrock 上的 Mistral Large、AWS Deadline Cloud 等(2024 年 4 月 8 日)

距离 AWS 悉尼峰会(4 月 10 日至 11 日)只有两天的时间,AWS 峰会东南亚季也将于一个月后拉开帷 […]

AWS将在新加坡和曼谷举办峰会,介绍了一些新的发布内容,包括Amazon EC2 G6实例、Mistral Large在Amazon Bedrock中的可用性、Amazon Aurora和Amazon Redshift的零ETL集成、AWS Deadline Cloud、AWS Clean Rooms ML、Amazon Bedrock知识库对OpenSearch无服务器的支持、Amazon EKS对Kubernetes版本的扩展支持、AWS Lambda对Ruby 3.3的支持、Amazon EventBridge控制台增强功能、商业区域对AWS管理控制台进行私有访问。

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Mouse Vs Python

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Anaconda Partners with Teradata for AI with Python packages in the Cloud

Anaconda has announced a new partnership with Teradata to bring Python and R packages to Teradata VantageCloud through the Anaconda Repository. But what does that mean? This new partnership allows engineers to: Rapidly deploy and operationalize AI/ML developed using open-source Python and R packages. Unlock innovation and the full potential of data at scale with a […] The post Anaconda Partners with Teradata for AI with Python packages in the Cloud appeared first on Mouse Vs Python.

Anaconda与Teradata合作,通过Anaconda Repository将Python和R包引入Teradata VantageCloud,允许工程师快速部署和操作使用开源Python和R包开发的AI/ML。用户可以在VantageCloud Lake上使用Python和R功能,解锁数据的全部潜力。用户可以选择包和版本,进行大规模数据科学、AI/ML和生成AI用例。用户可以安全地将Python/R模型与VantageCloud Lake一起使用,无知识产权泄漏。Teradata VantageCloud Lake客户可以免费从Anaconda Repository下载Python和R包。

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MongoDB

MongoDB -

Enabling Commerce Innovation with the Power of MongoDB and Google Cloud

Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success. Kin + Carta, a Premier Google Cloud Partner and MongoDB Systems Integrator Partner, recently launched the Integrated Commerce Network. The Integrated Commerce Network is an Accelerator that enables clients to modernize to a composable commerce platform and create value with their commerce data on Google Cloud with a pre-integrated solution in as little as six weeks. This article explains the concept of composable commerce and explores how MongoDB and Google Cloud form a powerful combination that enables innovation in commerce. Finally, it explains how Kin + Carta can help you navigate the complexity facing businesses today with their approach to digital decoupling. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More Unraveling the complexity: What is composable commerce? Why microservices and APIs? The evolution of commerce architecture Traditional monolithic architectures, once the cornerstone of commerce platforms, are facing challenges in meeting the demands of today's fast-paced digital environment. Microservices, a paradigm that breaks down applications into small, independent services, offer a solution to the limitations of monoliths. This architectural shift allows for improved agility, scalability, and maintainability. Defining composable commerce Composable commerce is a component-based, API-driven design approach that gives businesses the flexibility to build and run outstanding buying experiences free of constraints found in legacy platforms. To be truly composable, the platform must support key tenets: Support continuous delivery without downtime at the component level Have API as the contract of implementation between services, with open, industry-standard protocols providing the glue between components Be SaaS based, or portable to run on any modern public cloud environment Allow the open egress and ingress of data — no black-boxes of vendor data ownership Defining APIs and microservices APIs play a pivotal role in connecting microservices, enabling seamless communication and data exchange. This modular approach empowers businesses to adapt quickly to market changes, launch new features efficiently, and scale resources as needed. Enhanced scalability, resilience, and agility Taking a microservices approach provides businesses with options and now represents a mature and battle-tested approach with commoditized architectures, infrastructure-as-code, and open-source design patterns to enable robust, resilient, and scalable commerce workloads at lower cost and risk. Additionally, the decoupled nature of microservices facilitates faster development cycles. Development teams can work on isolated components, allowing for parallel development and quicker releases. This agility is a game-changer in the competitive e-commerce landscape, where rapid innovation is essential for staying ahead. Microservices and API-based commerce solutions (like commercetools, which is powered by MongoDB) have begun to dominate the market with their composable approach, and for good reason. These solutions remove the dead-end of legacy commerce suite software and enable a brand to pick and choose to enhance its environment on its own terms and schedule. MongoDB Atlas: The backbone of intelligent, generative AI-driven experiences As e-commerce has developed, customers are expecting more from their interactions — flat, unsophisticated experiences just don’t cut it anymore and brands need to deliver on the expectation of immediacy and contextual relevance. Taking a microservices approach enables richer and more granular data to be surfaced, analyzed, and fed back into the loop, perhaps leveraging generative AI to synthesize information that previously would have been difficult or impossible without huge computing capabilities. However, to do this well you need core data infrastructure that underpins the platform and provides the performance, resilience, and advanced features required. MongoDB Atlas on Google Cloud can play a pivotal role in this enablement. Flexible data models: Microservices often require diverse data models. MongoDB Atlas, a fully managed database service, accommodates these varying needs with its flexible schema design, which allows businesses to adapt their data structures without compromising performance. Horizontal scalability: Modern commerce moves a lot of data. MongoDB Atlas excels in distributing data across multiple nodes, ensuring that the database can handle increased loads effortlessly. Real-time data access: Delivering on expectations relies on real-time data access. MongoDB Atlas supports real-time, event-driven data updates, ensuring you are using the most up-to-date information about your customers. Serverless deployment: Rather than spend time and money managing complex database infrastructure, MongoDB Atlas can leverage serverless deployment, allowing developers to focus on building features that delight customers and impact the bottom line. Unleashing generative AI with MongoDB and Google Cloud Generative AI applications thrive on massive datasets and require robust data management. MongoDB effortlessly handles the complex and ever-evolving nature of gen AI data. This includes text, code, images, and more, allowing you to train your models on a richer data tapestry. MongoDB Atlas: Streamlined gen AI development on Google Cloud MongoDB Atlas, the cloud-based deployment option for MongoDB, integrates seamlessly with Google Cloud. Atlas offers scalability and manageability, letting you focus on building groundbreaking gen AI applications. Here's how this powerful duo functions together: Data ingestion and storage: Effortlessly ingest your training data, regardless of format, into MongoDB Atlas on Google Cloud. This data can include text for natural language processing, code for programming tasks, or images for creative generation. AI model training: Leverage Google Cloud's AI services like Vertex AI to train your gen AI models using the data stored in MongoDB Atlas. Vertex AI provides pre-built algorithms and tools to streamline model development. Operationalization and serving: Once trained, deploy your gen AI model seamlessly within your application. MongoDB Atlas ensures the smooth data flow to and from your model, enabling real-time generation. Vector search with MongoDB Atlas: MongoDB Atlas Vector Search allows for efficient retrieval of similar data points within your gen AI dataset. This is crucial for tasks like image generation or recommendation systems. Advantages of this open approach By leveraging a microservices architecture, APIs, and the scalability and flexibility of Atlas, businesses can build agile and adaptable composable platforms. Atlas seamlessly integrates with Google Cloud, providing a streamlined environment for developing and deploying generative AI models. This integrated approach offers several benefits: Simplified development: The combined power of MongoDB Atlas and Google Cloud streamlines the development process, allowing you to focus on core gen AI functionalities. Scalability and flexibility: Both MongoDB Atlas and Google Cloud offer on-demand scalability, ensuring your infrastructure adapts to your gen AI application's growing needs. Faster time to market: The ease of integration and development offered by this combination helps you get your gen AI applications to market quickly. Cost-effectiveness: Both MongoDB Atlas and Google Cloud offer flexible pricing models, allowing you to optimize costs based on your specific gen AI project requirements. Digital decoupling, a legacy modernization approach With so much digital disruption, technology leaders are constantly being challenged. Existing legacy architectures and infrastructure can be extremely rigid and hard to unravel. Over 94% of senior leaders reported experiencing tech anxiety. So how do you manage this noise, meet the needs of the business, stay relevant, and evolve your technology so that you can deliver the kinds of experiences audiences expect? Digital decoupling is a legacy modernization approach that enables large, often well-established organizations to present a unified online experience to their users, take full advantage of their data, innovate safely, and compete effectively with digital natives. Technology evolves rapidly, and an effective microservices solution should be designed with future scalability and adaptability in mind. Kin + Carta helps to ensure that your solution is not only robust for current requirements but also capable of evolving with emerging technologies and business needs. It all starts with a clear modernization strategy that allows you to iteratively untangle from legacy systems, while also meeting the needs of business stakeholders seeking innovation. Navigating commerce complexity with Kin + Carta on Google Cloud Commerce is undergoing a significant transformation, and businesses need a future-proof technology foundation to handle the demands of complex models and massive datasets. That’s why Kin + Carta launched their Integrated Commerce Network, the first commerce-related solution that’s part of Google’s Industry Value Network. With the right tools and partners, your business can be at the forefront of innovation with generative AI, through automating tasks in revolutionary new ways, creating entirely new content formats, and delivering more personalized customer experiences. The complexities of commerce transformation can be daunting. But you can master the art of digital decoupling and leverage the strengths of the Integrated Commerce Network to unlock limitless possibilities and gain an edge over your competition. Check out Kin + Carta’s guide: Flipping the script — A new vision of legacy modernization enabled by digital decoupling. Get started with MongoDB Atlas on Google Cloud today.

Kin + Carta推出了Integrated Commerce Network,该加速器允许客户在短短六周内现代化其商务平台,并在Google Cloud上利用其商务数据。文章介绍了可组合商务的概念,微服务和API的好处,以及MongoDB和Google Cloud在商务创新方面的强大组合。它还强调了MongoDB Atlas在实现生成式AI驱动体验方面的作用。文章最后讨论了开放方法的优势和在现代化遗留系统时数字解耦的重要性。

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MongoDB

MongoDB -

A Smarter Factory Floor with MongoDB Atlas and Google Cloud's Manufacturing Data Engine

The manufacturing industry is undergoing a transformative shift from traditional to digital, propelled by data-driven insights, intelligent automation, and artificial intelligence. Traditional methods of data collection and analysis are no longer sufficient to keep pace with the demands of today's competitive landscape. This is precisely where Google Cloud’s Manufacturing Data Engine (MDE) and MongoDB Atlas come into play, offering a powerful combination for optimizing your factory floor. Unlock the power of your factory data MDE is positioned to transform the factory floor with the power of cloud-driven insights. MDE simplifies communication with your factory floor, regardless of the diverse protocols your machines might use. It effortlessly connects legacy equipment with modern systems, ensuring a comprehensive data stream. MDE doesn't just collect data, it transforms it. By intelligently processing and contextualizing the information, you gain a clearer picture of your production processes in real-time with a historical pretext. It offers pre-built analytics and AI tools directly addressing common manufacturing pain points. This means you can start finding solutions faster, whether it's identifying bottlenecks, reducing downtime, or optimizing resource utilization. Conveniently, it also offers great support for integrations that can further enhance the potential of the data (e.g. additional data sinks). The MongoDB Atlas developer data platform enhances MDE by providing scalability and flexibility through automated scaling to adapt to evolving data requirements. This makes it particularly suitable for dynamic manufacturing environments. MongoDB’s document model can handle diverse data types and structures effortlessly while being incredibly flexible because of its native JSON format. This allows for enriching MDE data with other relevant data, such as supply chain logistics, for a deeper understanding of the factory business. You can gain immediate insights into your operations through real-time analytics, enabling informed decision-making based on up-to-date data. While MDE offers a robust solution for collecting, contextualizing, and managing industrial data, leveraging it alongside MongoDB Atlas offers tremendous advantages Inside the MDE integration Google Cloud’s Manufacturing Data Engine (MDE) acts as a central hub for your factory data. It not only processes and enriches the data with context, but also offers flexible storage options like BigQuery and Cloud Storage. Now, customers already using MongoDB Atlas can skip the hassle of application re-integration and make this data readily accessible for applications. Through this joint solution developed by Google Cloud and MongoDB, you can seamlessly move the processed streaming data from MDE to MongoDB Atlas using Dataflow jobs. MDE publishes the data via a Pub/Sub subscription, which is then picked up by a custom Dataflow job built by MongoDB. This job transforms the data into the desired format and writes it to your MongoDB Atlas cluster. Google Cloud’s MDE and MongoDB Atlas utilize compatible data structures, simplifying data integration through a shared semantic configuration. Once the data resides in MongoDB Atlas, your existing applications can access it seamlessly without any code modifications, saving you time and effort. The flexibility of MDE, combined with the scalability and ease of use of MongoDB Atlas, makes this a powerful and versatile solution for various data-driven use cases such as predictive maintenance and quality control, while still providing factory ownership of the data. Instructions on how to set up the dataflow job are available in the MongoDB github repository. Conclusion If you want to level up your manufacturing data analytics, pairing MDE with MongoDB Atlas provides a proven, easy-to-implement solution. It's easy to get started with MDE and MongoDB Atlas. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More

制造业正经历数字化转型,谷歌云的制造数据引擎(MDE)和MongoDB Atlas提供强大的组合,优化工厂生产。MDE通过云驱动的洞察力改变工厂生产,连接传统设备和现代系统,提供全面的数据流。MongoDB Atlas通过自动扩展提供可扩展性和灵活性。MDE和MongoDB Atlas是数据驱动用例的强大解决方案。

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NVIDIA Blog

NVIDIA Blog -

Start Up Your Engines: NVIDIA and Google Cloud Collaborate to Accelerate AI Development

NVIDIA and Google Cloud have announced a new collaboration to help startups around the world accelerate the creation of generative AI applications and services. The announcement, made today at Google Cloud Next ‘24 in Las Vegas, brings together the NVIDIA Inception program for startups and the Google for Startups Cloud Program to widen access to Read Article

NVIDIA和Google Cloud宣布合作,为NVIDIA Inception初创企业计划和Google for Startups Cloud计划的成员提供云积分、市场推广支持和技术专业知识,以加速生成式AI应用和服务的创建。

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5 Workspace announcements from Google Cloud Next '24

Abstract blue, red, yellow and green shapes. In the center are the words “Google Workspace.”

Google Workspace在Cloud Next活动上发布了一些更新,包括使用Google Vids创建和分享视频,Meet中自动翻译字幕,新的AI安全插件保护敏感文件,新的Sheets和Docs功能组织信息,以及新的Gmail功能发送邮件。这些更新将逐渐在Google Workspace中推出。

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Cloud Next 2024: More momentum with generative AI

Colorful graphic that says “Google Cloud Next”

谷歌云在AI领域投资巨大,已有60%的AI初创公司和近90%的AI独角兽是其客户。谷歌云推出了Gemini 1.5 Pro和Gemma等强大的AI模型,提供了AI超级计算机和云安全功能。谷歌云还将AI功能引入Google Workspace,与梅赛德斯-奔驰和优步等公司合作推动各行业的AI创新。

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