Google Cloud stories
That could cut cloud migration risk and planning time for firms moving workloads across Amazon Web Services, Azure and Google Cloud Platform.
The centre is aimed at helping consumer firms turn AI pilots into operational tools for retail, travel and supply-chain tasks.
By isolating early tests from its core systems, the video platform hopes to turn brittle AI ideas into user research in weeks rather than months.
By shifting orchestration to Google Cloud's managed service, the payments firm cut monthly data platform costs by about 30% and improved reliability.
Early cloud deployments and benchmark gains signal NVIDIA's latest rack-scale AI platform is moving into commercial use as data centres chase efficiency.
Throughput for AI vector searches can rise fourfold on AlloyDB, as Google's preview aims to cut latency without changing pgvector code.
Developers may get patched code faster as Google's preview agent scans repositories, tests exploits and drafts fixes for review.
Cloud operators can now use IAM conditions to curb privilege escalation and narrow access to MCP tools, BigQuery and logs.
More than 1,000 engineers and executives are expected in Warsaw as digital firms face mounting pressure to keep systems fast and reliable at scale.
Downtime risk is lower for apps running across multiple regions, as traffic can now shift to a healthy Cloud Run region within seconds.
Cloud-based testing could cut prototype costs as Panasonic Automotive brings cockpit software validation closer to production hardware.
Businesses could cut AI data-handling costs sharply as NetApp adds DataPelago's zero-copy processing to its storage platform.
Developers can now use Google Cloud's examples to build and govern Gemini-powered agents for approvals, compliance and data workflows.
The preview could ease multi-region compliance headaches by letting organisations apply column-level security through global tags and regional policies.
Enterprises are turning to AI agents that can handle complex service requests, raising the stakes for vendors competing on reliability and governance.
Developers are being told to trim AI context and automate routine checks as token-heavy workflows raise latency, errors and cloud costs.
The framework targets CISOs and platform teams as they move AI systems into production, amid rising risks from prompts, models and outputs.
Security teams are being warned to keep humans and strict controls in place as AI agents can miss context and leak sensitive code.
The new workflow could cut model migration from months to hours, easing the testing burden for businesses running multiple AI features.
Notebook users can now query local pandas data with SQL and pass results back to Python, reducing data shuffling in analytics workflows.
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