Author: Cyrus Wong (黃俊彥) — Google Developer Expert - Cloud (AI), Senior Lecturer at HKIIT ...

Scale machine learning directly in Pandas and boost productivity using enterprise-grade BigQuery AI functions without leaving your DataFrame.

Act. You can stay. After June 30, 2027, PHP 8.2 functions cannot be created or updated, and may be disabled.

Act. You can stay. After May 10, 2027, .NET 8 functions cannot be created or updated, and may be disabled.

Act. You can stay. After April 4, 2027, Python 3.10 functions cannot be created or updated, and may be disabled.

Act. You can stay. After April 1, 2027, Go 1.25 functions cannot be created or updated, and may be disabled.

What Happened Four AWS changes affect how teams start, run, and maintain AI and cloud workloads: SageMaker Studio: New customers can sign in with Google, GitHub, or Apple and start…

Act. You can stay. After March 2, 2027, Go 1.24 functions cannot be created or updated, and may be disabled.

A fast, highly scalable, cost-effective, and fully managed cloud data warehouse for analytics, with built-in machine learning.

BoxLang 1.18.0 adds Azure Functions, cluster-safe scheduled tasks, a more automation-friendly CLI, and refreshed docs for developers and AI agents.

The automotive industry is rapidly transitioning to software-defined vehicles, moving core functions from physical hardware to digital systems. While infotainment and high-performance computing systems are often developed using cloud-native methods, many crucial microchips and control modules remain hardwired. This creates a significant bottleneck, making updates and expansion difficult post-manufacturing and pushing costly defect resolution to the end of development cycles. Google, through partnerships like with RemotiveLabs and its Horizon platform, aims to address these inefficiencies. Android Automotive OS (AAOS) already leverages virtual testing environments like Cuttlefish on Arm-based Google Cloud instances for seamless cockpit software development. RemotiveTopology extends this virtual model across the entire vehicle's electrical and electronic (E/E) architecture. By converting vehicle networks into "vehicle-as-code," RemotiveTopology creates a full, simulated E/E architecture that connects AAOS to other vehicle systems. This allows developers to spin up a complete virtual vehicle on demand, enabling continuous signal and cross-domain visibility. The system, built on standard Linux tooling, runs on various platforms, from laptops to Google Cloud. This approach facilitates early, rather than late, integration of all onboard systems within a virtual environment before physical hardware is involved. Critical, multi-domain interactions, such as ADAS events appearing in the infotainment cluster, can be tested continuously from the start. This early integration significantly reduces the likelihood and cost of discovering defects later in the development process. The ultimate goal is to enable scalable software development for even the smallest vehicle components, bridging the gap to fully software-defined vehicles. Google Cloud's Arm-based infrastructure, including Axion's bare-metal C4A instances, ensures parity between cloud-developed software and actual vehicle control modules. This holistic system allows for the first time, full vehicle system development and integration testing in the cloud, directly transferable to physical vehicles. The benefits are substantial, including over 10x faster integration cycles, as measured in early customer engagements. Virtual systems are crucial for keeping pace with AI-assisted workflows, which generate, test, and refine vehicle software rapidly. RemotiveTopology makes this architecture accessible, offering an open-source simplified vehicle on GitHub for immediate use without new third-party tooling. Manufacturers can leverage existing toolchains and easily integrate RemotiveTopology into Arm-based Google Cloud environments. For those currently limiting virtual development to the cockpit, it's time to expand to the entire vehicle in the cloud through this innovative approach.

# ZOZO TECH BLOG ZOZO # TECH BLOG # 新米Google Cloud管理者の奮闘記のその後 〜Organizationの秩序を維持する試み〜 BigQuery Google Cloud こんにちは、技術本部ML・データ部データ基盤ブロックの塩崎です。最近の気になる論文は、こちら1の動物病院での猫のストレスが音楽によって低減されるというものです。 さて、2年前にGCPの新米管理者になり色々と頑張っていますという内容のブログを公開しました。当時は対応が後手に回ってしまっていた内容でしたが、その後2年が経ったので、最近のGoogle Clou

Kdy se vyplatí serverless v Azure? Azure Functions, Flex Consumption plán, cold start a pricing v praxi s C# i Pythonem. Serverless model vs. App Service a Container Apps – škáluje se exekuce, ne instance Kdy serverless použít (nárazová zátěž, event-driven workflow, plánované úlohy) a kdy ne Pricing v GB-sekundách, free grant a srovnání s AWS Lambda a Google Cloud Run Functions…

BigQuery generative AI functions allow users to integrate large language models directly into SQL queries for tasks like classification and sentiment analysis. These functions are billed per token, making proactive budget management crucial for large-scale use. Google Cloud has introduced token-based cost management for these functions through daily quotas managed via IAM. Four daily quotas exist: AI input tokens per day, AI input tokens per day per user, AI output tokens per day, and AI output tokens per day per user. These quotas apply globally and cover specific generative AI functions in BigQuery, excluding embedding functions. Administrators can view and adjust quotas directly in the Google Cloud console or programmatically through APIs. To manage costs effectively, users can lower quotas immediately or request increases, which are often auto-approved. Monitoring usage on the Quotas & System Limits page helps detect spikes and right-size budgets. Token quotas act as a preventive, AI-specific guardrail, distinct from broader Cloud Billing spend caps. Enforcement involves stopping work when quotas are reached, either rejecting queries upfront or halting LLM calls mid-query. The AI.COUNT_TOKENS function aids in estimating input token usage without incurring extra costs. By understanding token pricing and estimating usage, administrators can set appropriate quota values to align with budget goals. Best practices include starting with per-user quotas, measuring input data with AI.COUNT_TOKENS, being mindful of the higher cost of output tokens, and iteratively adjusting limits based on observed usage. Implementing these token-based guardrails provides cost predictability for generative AI in BigQuery.

As a member of Google’s Government Affairs and Public Policy team, you'll be part of a global government affairs team, working across regions, product areas, and functions. You’ll combine creativity a...

A fast, highly scalable, cost-effective, and fully managed cloud data warehouse for analytics, with built-in machine learning.

Token-based cost management for BigQuery generative AI functions lets you set daily quotas on AI token consumption so you can keep within your budget.

### 量子コンピューティングとの統合 AWS Braket、Azure Quantum、Google Quantum AIなど、各クラウドベンダーが量子コンピューティングサービスを提供しています。量子コンピューティングが実用段階に入れば、マルチクラウド戦略に量子コンピューティングが組み込まれることになるでしょう。 ### サーバーレスマルチクラウドの普及 サーバーレスアーキテクチャのさらなる普及により、マルチクラウドの実現がより容易になると見込まれます。AWS Lambda、Azure Functions、Google Cloud Functionsを横断的に活用するフレームワークの成

Google launched Google AI Edge Foresight, a Mac application that functions entirely offline using local AI models to capture meeting notes and query data via a Gemma 4 assistant. The tool supports transcription for Google Meet and Zoom, integrates with various document formats, and ensures privacy by processing audio locally without cloud synchronization.