All news with #google cloud tag

🔌 AWS now offers a free 500 Mbps Interconnect - multicloud tier to simplify private connectivity between AWS and other public clouds. The open specification behind Interconnect is already adopted by Google Cloud and Oracle Cloud Infrastructure, with Microsoft Azure planned later in 2026. The free tier provides a fully managed, resilient path (one local Tier 1 Interconnect per customer per region) and includes an Amazon CloudWatch Network Synthetic Monitor at no additional cost. Other CSPs set their own charges for their side of the link, so customers should review third-party pricing before creating an Interconnect.

🛠️ App-centric maintenance visibility in Unified Maintenance shifts focus from infrastructure to business services. By integrating with App Hub, Unified Maintenance aggregates maintenance schedules for registered resources—GKE clusters, GCE VMs, AlloyDB instances—into a single application-aware dashboard. This reduces manual mapping, speeds triage of performance issues against planned updates, and helps platform teams predict operational impacts across many projects.

🧭 Google describes how innovations from its internal Flume platform power Dataflow, a fully managed batch and streaming service supporting large-scale ML workloads. The post outlines features like liquid sharding for dynamic rebalancing, global compute for cross-region scaling, automatic pipeline optimization, and rate-limiting for external API calls. It also highlights TPU-focused efficiencies such as heterogeneous worker pools, TPU-aware autoscaling, duty-cycle enforcement, and TPU fungibility. The article notes developer conveniences—multi-language SDKs, unified batch/streaming, ML framework integration, observability, and advanced workflows—and cites customer use cases and ongoing platform enhancements.

🧭 This article examines practical strategies to reduce AI cold-start latency on Cloud Run when serving GPU-backed models. It outlines the four-phase cold-start process, highlights storage and model-format choices (Cloud Storage, container images, GGUF, Safetensors, quantization), and explains Cloud Run features like image streaming, temporary CPU boosts, and concurrency tuning. The piece also shares operational tactics—warmup endpoints, startup probe tuning, regional deployment choices—and production patterns used by Elastic to treat GPUs as fungible compute.

🦷 Movix built a custom agentic AI platform to address a severe shortage of skilled dental technicians and reduce costly remakes in aligner and appliance manufacturing. Using Google Cloud infrastructure, including Gemini Enterprise Agent Platform, Cloud Run with L4 GPUs, and Compute Engine, Movix developed deep learning, computer vision, and 3D mesh models to automate quality control and data entry. The solution integrates with legacy lab systems, anonymizes PHI for compliance, and targets large-volume labs to improve accuracy, speed, and cost savings.

🔔 AppLifecycle Manager Feature Flags (ALM FF) enters public preview as a rule-based service to decouple feature releases from code deployments. By using toggles and the Common Expression Language (CEL), teams can perform gradual rollouts, instant kill-switches, and percentage-based traffic ramps. String-type flags enable dynamic configuration for applications, including LLM prompts, while OpenFeature compatibility avoids vendor lock-in.

🚀Urban Outfitters, Inc. (URBN) recently migrated its IBM Sterling OMS from an 11TB Oracle backend to Google Cloud’s AlloyDB for PostgreSQL to reduce TCO and improve scalability and performance. The migration was executed through close collaboration among URBN, IBM, and Google Cloud, with embedded engineering teams driving planning, testing, and tuning. Outcomes included optimized storage and compute, two read replicas for higher availability, significant performance improvements, and a shift toward open standards to future-proof operations.

🔒 This article outlines five architectural patterns for exposing enterprise data to autonomous systems on Google Cloud, using BigQuery examples and mocked CRM data as pedagogical blueprints. It contrasts deterministic, developer-authored SQL APIs with agentic approaches that use LLMs, platform-native reasoning like the Conversational Analytics API, and the vendor-neutral Model Context Protocol (MCP). It highlights trade-offs in trust, complexity, cost, latency, and maintenance.

🚀 Google Cloud announced streaming AI enhancements to its Agentic Data Cloud at Next ‘26, unifying Pub/Sub, Dataflow, BigQuery, Bigtable and Managed Service for Kafka to deliver real-time context and low-latency inference. These additions include Pub/Sub AI inference, BigQuery continuous queries for stateful stream processing, Pub/Sub→Bigtable subscriptions, and unified embedding sinks for immediate semantic search and agent memory. The platform also supports MCP and ADK integrations so agents can manage resources and run inside Dataflow pipelines, reducing context lag for use cases like fraud detection and autonomous supply chain actions.

📱Glance built an automated pipeline to convert long-form landscape videos into short, vertical clips optimized for mobile lock screens. The system uses Google Cloud Speech-to-Text v2, Gemini, and the Vision API together with Samurai, OpenCV and MoviePy to identify key moments, detect active speakers, and reframe shots intelligently. It supports split-screen stacking, word-level “Karaoke-style” captions, automated branding overlays, and smoothing techniques to scale production from thousands to tens of thousands of daily clips.

🏥 The post argues that modern cloud infrastructure is the superior foundation for regulated Software as a Medical Device (SaMD), enabling faster innovation while meeting regulatory obligations. It outlines regulatory shifts in early 2026, including the FDA's QMSR alignment with ISO 13485 and the EU AI Act's applicability for high-risk systems. The author advocates Compliance as Code and describes three architectural planes—data, control, and evidence—on Google Cloud to deliver continuous audit readiness. It also highlights AI-driven monitoring and a shared fate model between cloud providers and manufacturers.

🔍 Google Cloud presents a SIGMOD paper introducing proxy models—cost‑optimized, ultra‑lightweight models that replace most LLM calls in AI-powered SQL functions. They rely on precomputed embeddings (using Gemini) and simple classifiers (currently logistic regression) to deliver orders‑of‑magnitude reductions in latency and token costs. BigQuery and AlloyDB implement this optimization with online training in BigQuery and PREPARE-based offline training in AlloyDB. The technique performs well for many semantic filters but can fail on tasks requiring complex reasoning or extreme selectivity.

🔔At SAP SAPPHIRE, Google Cloud and SAP introduced a Unified Data Foundation to connect SAP business data directly into BigQuery and enable agentic AI workflows. Announcements include BDC Connect for BigQuery GA with zero-copy access, new 48TB X5 memory-optimized instances, a SecNumCloud-qualified Sovereign Cloud with S3NS, and Google SecOps for SAP in preview. The new Cortex Framework preview aims to accelerate building agentic solutions while maintaining enterprise governance and reducing data movement.

🚀 Imgix serves over 8 billion images and videos daily and has migrated its real-time processing stack to G4 VMs on Google Cloud, powered by NVIDIA RTX PRO 6000 Blackwell GPUs. The move delivered a 50% reduction in median latency and a 5–6× increase in throughput per node without rewriting core application code. Imgix combines nvJPEG, NVENC/NVDEC, custom Vulkan compute shaders and CUDA libraries to accelerate decoding, transformation and encoding, while autoscaling, self-healing GPU management and a 2.5PB GCS cache enable fast, reliable global delivery.

📊 Database Center now offers an AI-native manageability interface powered by Gemini, delivering fleet-wide visibility across Google Cloud managed databases. It introduces Gemini-backed recommendation validation (coming soon) to simulate performance impacts before applying changes like new indexes or machine upgrades. Additional enhancements include inventory, end-to-end lineage, and automated health checks to reduce MTTR and operational overhead.

🚀 Cloud Storage Rapid introduces high-performance object storage for AI and analytics with Rapid Bucket (zonal, GA) and Rapid Cache (accelerated reads for existing buckets). It combines sub-millisecond latency, multi-TB/s throughput, and new semantics like native appends and vectored reads to reduce I/O bottlenecks that stall GPUs/TPUs. Rapid Cache now supports ingest-on-write to eliminate initial cache-miss penalties and improve checkpoint restores, while multi-region buckets preserve a single namespace and avoid manual data movement.

🚀 AlloyDB now offers general availability of PostgreSQL 18 and introduces Extended Support for older major versions, giving customers flexibility to upgrade on their own schedule. Extended Support enrolls eligible clusters automatically for three years and provides critical security patches, proactive bug fixes, SLA coverage, and the ability to provision new clusters on supported legacy releases. AlloyDB also supports in-place major version upgrades, incorporates PG18 performance features, and leverages a separated compute/storage architecture to improve price-performance and scalability.

🔐 At Google Cloud Next, Google outlined a resilient, scalable, and secure foundation to accelerate public sector adoption of the agentic era, highlighting infrastructure, data, and security innovations. Key infrastructure announcements include the AI Hypercomputer with eighth-generation TPUs (TPU 8t for training, TPU 8i for inference) and Virgo Networking, plus Google Distributed Cloud bringing Gemini to where data resides. On data, an AI-native architecture features Knowledge Catalog (FedRAMP High, DoD IL4 & IL5) and a cross-cloud Lakehouse to ground agents in trusted context. Security advances combine Google Threat Intelligence with Wiz, authorize Cloud Armor and Model Armor, and add defensive agents to protect models and sensitive data.

🔁 BASF created a digital twin of its complex two‑year agricultural supply chain using AlphaEvolve on Google Cloud. The evolutionary AI began from a seeded planning program and ingested three years of historical production, inventory, and demand data to autonomously evolve planning logic. Resulting algorithms closely mirrored historical inventory and production behavior, improving accuracy by over 80% versus the seed and producing human‑readable rules to support planners.

⚡ Google Cloud announced the Bigtable in-memory tier at Google Cloud Next ’26, adding an integrated hot-data layer that delivers sub-millisecond read latency, roughly 10× higher point-read throughput per dollar, and up to 120,000 QPS on a single row. Backed by RDMA and a hybrid RAM/SSD/HDD architecture, Bigtable auto-promotes hot rows into memory and preserves durability and enterprise controls. The capability is available in the new Bigtable Enterprise Plus edition, reducing operational cache complexity and improving TCO.