# Cloud, Platform Published articles for Cloud, Platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI-Ready Private Cloud with Cisco and VMware DevFeed: [AI-Ready Private Cloud with Cisco and VMware]() Original publisher: [Read original article]() Author: sabina anja Published: 2026-09-08T15:33:39Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [Network](), [Cloud](), [networking](), [cloud-infrastructure](), [distributed-training](), [datacenter](), [GPU](), [Latency](), [Inference](), [model-deployment](), [inference-endpoints]() Tags: [ai](), [cisco](), [cloud](), [cloud-infrastructure](), [cloud-platform](), [data-center](), [distributed-training](), [fabric](), [gpu](), [home-page](), [inference](), [inference-endpoints](), [latency](), [networking](), [object-storage](), [pipelines](), [private-cloud](), [telemetry](), [vcf-9-1](), [vcf-networking](), [vmware](), [vmware-cloud-foundation]() ### AI overview This article explains why an AI-ready private cloud requires more than adding GPUs. It focuses on how Broadcom and Cisco are integrating VMware Cloud Foundation with Cisco Nexus One Fabric to address AI workload networking, including bandwidth-intensive east-west traffic, bursty north-south traffic, latency, congestion management, and telemetry across virtual and physical infrastructure. ### Source excerpt An AI-ready private cloud is not simply a private cloud with GPUs added to it. What determines whether a private cloud platform can actually serve AI workloads effectively is everything built around them: how the fabric carries traffic, how the tenancy model lets teams consume capacity, and how policy and telemetry stay coherent across the ... Continued The post AI-Ready Private Cloud with Cisco and VMware appeared first on VMware Blogs. ## Azure Private Link for Elastic Cloud Serverless is now generally available DevFeed: [Azure Private Link for Elastic Cloud Serverless is now generally available]() Original publisher: [Read original article]() Author: Alex Chalkias,Jordi Mon Companys Published: 2026-08-04T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Elastic Cloud]() Tags: [aws](), [azure](), [cloud-platform](), [dns](), [elastic-cloud](), [how-to](), [policy](), [release](), [security-compliance-cloud-security](), [serverless]() ### AI overview Azure Private Link for Elastic Cloud Serverless is generally available. It enables private connectivity from Azure VNets to Serverless projects, with connection policies enforcing private-endpoint access. ### Source excerpt Azure Private Link for Elastic Cloud Serverless is generally available. Your traffic stays inside Azure's private backbone -- no public internet exposure required. ## DataAgents: How we turned 9 months of analysis into 10 days DevFeed: [DataAgents: How we turned 9 months of analysis into 10 days]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-09T22:42:18Z Content type: tutorial Language: en Sources: [Capital One Tech]() Topics: [Cloud](), [AI Agent](), [Amazon Web Services](), [Azure](), [Google Cloud Platform (GCP)](), [data]() Tags: [agentic-ai](), [ai](), [aws](), [azure](), [cloud](), [cloud-platform](), [data-analysis](), [false-positives](), [gcp](), [least-privilege](), [security]() ### AI overview This engineering deep dive describes the DataAgents pattern for analyzing heterogeneous cloud resources at scale. It focuses on cloud resource dormancy detection across AWS, Azure, and Google Cloud Platform, using entity-specific criteria, confidence-based prioritization, and documented reasoning. The article reports reducing the analysis effort from an estimated 6-9 months to 10 days. ### Source excerpt An engineering deep dive into the pattern that changed how we approach large-scale classification problems. Every engineering team has that project sitting in the backlog. The one where someone says, "We really should analyze all of these," and the room goes quiet. Everyone knows what "all of these" means -- hundreds of entities, complex rules, no clear starting point. For us, it was cloud resource dormancy detection. We had around 350 distinct cloud resource types spread across AWS, Azure and Google Cloud Platform (GCP). Each type has different behavior patterns. An EC2 instance sitting idle looks nothing like a dormant Amazon S3 (S3) bucket or an unattached Elastic IP. Detecting dormancy required understanding what "active" means for each specific resource, then writing detection logic that wouldn't flood operations teams with false positives. Traditional estimate: 6-9 months of expert analysis. Actual time: 10 days. Here's how we did it, and more importantly, here's the reusable pattern behind it. The problem with large-scale analysis: Before we get to the solution, it's worth naming the pattern that makes these projects so painful. It shows up everywhere: Cloud resources - Which of our 350 resource types are dormant? Data governance - Which of our 800 tables have quality issues we should monitor? Security - Which of our access entitlements violate least-privilege principles? Compliance - Which of our 500 policy controls need remediation? In every case, the structure is similar -- a large catalog of heterogeneous entities, entity-specific rules that don't generalize, unknown priorities and a high cost for getting it wrong. The traditional approach is not just slow. It's structurally limited. You get coverage of the "obvious" cases, inconsistent logic across analysts, and tribal knowledge that evaporates when people leave. What you need is something that can assess each entity, apply consistent criteria, prioritize by confidence and document its reasoning. The DataA ## The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases DevFeed: [The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases]() Original publisher: [Read original article]() Author: Joe Keegan Published: 2026-04-17T20:10:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Inference](), [Databases](), [Cloud](), [Artificial Intelligence](), [MongoDB](), [PostgreSQL](), [valkey]() Tags: [agent](), [agentic](), [ai](), [cloud](), [cloud-platform](), [databases](), [digitalocean](), [engineering](), [inference](), [mongodb](), [platform](), [postgresql](), [stateful](), [valkey]() ### AI overview This technical article presents DigitalOcean Managed Databases as the memory layer for an inference cloud. It explains how PostgreSQL, MongoDB, and Valkey can serve as systems of record for stateful AI applications, supporting persistent context, recovery in multi-stage workflows, and access to organization-specific data. ### Source excerpt As AI moves from experimental chat interfaces to production-grade agents, the need for a foundational memory layer to transform these AI-powered tasks into stateful models is apparent. The absence of a robust memory layer causes agents to lose vital statefulness, leading to: Inability to maintain long-term recall. Without persistent memory to track context across sessions, an agent might recognize specific user preferences in January but fail to apply that data months later, requiring the user to repeat the entire briefing. Vulnerability in multi-stage workflows. Lacking durable execution, there is no "save point" for recovery; consequently, a simple network interruption forces complex agentic processes, such as gathering diagnostic data via multiple tool calls, to restart entirely rather than resume from the point of failure. Disconnect from business-specific realities. If an agent cannot access private internal records or real-time operational data, it relies on general training data and guesswork, often confidently fabricating generic policies or specifications that are factually inaccurate for your organization. DigitalOcean is constantly evolving to meet this challenge, and we've entered the era of the inference cloud: A full-stack cloud platform purpose-built to run AI in production. With Gradient™AI Platform providing the specialized compute for AI applications, DigitalOcean Managed Databases serves as the foundational memory layer. Offerings from PostgreSQL, MongoDB, and Valkey function as the system of record for today's stateful AI applications, particularly so when they're connected to the DigitalOcean Agentic Inference Cloud. What is the inference cloud? The need for an inference cloud stems from a fundamental shift in how AI is being built, deployed, and used in 2026. For years, the industry's focus was on training or the capital-intensive process of building a model. But now developers are shifting to running that pre-trained model in a live product. T ## Threat Modeling Google Cloud (Threat Model Thursday) DevFeed: [Threat Modeling Google Cloud (Threat Model Thursday)]() Original publisher: [Read original article]() Author: Adam Published: 2023-03-01T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Google Cloud Platform (GCP)](), [Security](), [Google](), [Architecture & Design]() Tags: [assets](), [cloud](), [cloud-platform](), [data](), [diagram](), [google](), [google-cloud](), [google-cloud-platform](), [processes](), [security]() ### AI overview The article reviews NCC's threat model for Google Cloud Platform, examining its methodology, diagrams, assets, boundaries, threat actors, attack goals, weaknesses, and potential threats. It offers constructive observations about the model's organization, readability, and use of STRIDE-related categories. ### Source excerpt NCC has released a threat model for Google Cloud Platform. What can it teach us? ## Capacity Planning at Scale DevFeed: [Capacity Planning at Scale]() Original publisher: [Read original article]() Author: Kathryn Tang Published: 2020-12-03T18:00:01Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Scalability](), [cloud-infrastructure](), [Google](), [log management]() Tags: [cloud](), [cloud-platform](), [data](), [resources](), [safety](), [scalability](), [shopify](), [storage]() ### AI overview Shopify describes its capacity-planning process for the Black Friday-Cyber Monday period. The approach forecasts traffic, estimates Google Cloud resource requirements such as CPUs and storage, adds safety buffers, coordinates planning across teams, and validates the plans with scalability tests. ### Source excerpt We cover our approaches to capacity planning, and how we rolled it out across the org and to dozens of teams. We'll also share how we validated our capacity plans with scalability tests to make sure they work. ## How Shopify Manages Petabyte Scale MySQL Backup and Restore DevFeed: [How Shopify Manages Petabyte Scale MySQL Backup and Restore]() Original publisher: [Read original article]() Author: Akshay Suryawanshi Published: 2019-10-01T14:30:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [MySQL](), [Shopify](), [Google Cloud Platform (GCP)](), [cloud-infrastructure](), [API](), [Tooling](), [Availability]() Tags: [api](), [availability](), [backup](), [cloud](), [cloud-platform](), [cloud-storage](), [compute](), [data](), [disaster-recovery](), [infrastructure](), [kubernetes](), [mysql](), [scale](), [shopify](), [tooling]() ### AI overview Shopify redesigned its petabyte-scale MySQL backup and restore system on Google Cloud Platform by replacing file-based backups with Persistent Disk snapshots. The new tooling reduced recovery time from more than six hours per shard to under 30 minutes, while supporting frequent incremental snapshots across regions and Kubernetes-based operations. ### Source excerpt At Shopify, we need a robust backup and restore solution for our MySQL servers. We used disk-based snapshots to reduced our RTO to under 30 minutes. ## Engineering a Historic Moment: Shopify Gets Ready for Cannabis in Canada DevFeed: [Engineering a Historic Moment: Shopify Gets Ready for Cannabis in Canada]() Original publisher: [Read original article]() Author: Jason Hiltz-Laforge Published: 2019-02-07T16:30:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Google Cloud Platform (GCP)](), [Terraform](), [Cloud](), [Kubernetes](), [migration](), [data](), [Networks](), [DDoS]() Tags: [attacks](), [cloud](), [cloud-platform](), [clusters](), [compute](), [core](), [engineering](), [google-cloud](), [google-cloud-platform](), [industry](), [infrastructure](), [kubernetes](), [migration](), [networks](), [platform](), [resiliency](), [retail](), [shopify](), [terraform](), [warehouse]() ### AI overview Shopify describes the engineering work required to support cannabis retailers in Canada after legalization in 2018. The team built a Montreal-based regional deployment on Google Cloud Platform to satisfy Canadian data-residency requirements, using Google Kubernetes Engine, Google Compute Engine, Terraform, regional clusters, workload segregation, and a regional data warehouse. It also modeled traffic scenarios and provisioned capacity for launches, media-driven surges, sales peaks, and possible denial-of-service attacks. ### Source excerpt On October 17th, 2018, Canada ended a 95-year history of cannabis prohibition. For Shopify, the legalization of cannabis marked a new industry entering the Canadian retail market and we worked with governments and licensed sellers across the country to provide a safe, reliable and scalable platform for their business. For our engineering team, it meant significant changes to our platform to meet the strict requirements for this newly regulated industry. ## What's new at Firebase Summit 2018 DevFeed: [What's new at Firebase Summit 2018]() Original publisher: [Read original article]() Author: Francis Ma Published: 2018-10-29T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Cloud Platform (GCP)](), [Google]() Tags: [a-b-testing](), [app-development](), [app-quality](), [bigquery](), [cloud-functions](), [cloud-messaging](), [cloud-platform](), [community](), [crashlytics](), [data-studio](), [dynamic-audiences](), [firebase](), [firebase-summit](), [firestore](), [google-analytics](), [hosting](), [ios](), [live-streaming](), [machine-learning](), [ml-kit](), [news](), [performance-monitoring](), [predictions](), [realtime-database](), [remote-config](), [summit](), [tensorflow-lite](), [test-lab](), [unity](), [updates](), [web]() ### AI overview Firebase announces updates at Firebase Summit 2018, including beta support for Firebase through Google Cloud Platform support packages. The article also highlights Firebase adoption and a Hotstar case study involving increased user engagement. ### Source excerpt News, tutorials, and updates from the Firebase team.