Raluca Constantin

🧠 How JFrog ML Is Transforming the AI Software Supply Chain AI is becoming core to software delivery, but most ML pipelines remain fragmented, insecure and hard to govern. JFrog ML extends JFrog’s Software Supply Chain Platform to unify DevOps, DevSecOps and MLOps all in one secure enterprise-grade ecosystem. JFrog ML delivers:  ✅ End-to-end model lifecycle management ✅ Built-in versioning, metadata and promotion workflows ✅ Continuous security scanning of AI models and dependencies ✅ Seamless CI/CD integration for ML artifacts ✅ Multi-cloud and hybrid support with full traceability As AI scales, so do the risks. JFrog ML helps teams manage models with the same trust, traceability and automation as software, at an enterprise scale. 👉 Read the full blog here: https://lnkd.in/gkPGEfnP #MLOps #DevSecOps #SoftwareSupplyChain #AI #JFrog

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Kubernetes makes multi-tenancy easy—but chargeback? Not so much. Our K8s Guide, "Kubernetes Chargeback," explains how to build a cost allocation and reporting model for Kubernetes that engineering and finance can trust. If you're scaling Kubernetes across teams or business units, this is your playbook for turning shared infrastructure into shared accountability. 📘 Read the guide: https://lnkd.in/ea3nTuE3 #Kubernetes #DevOps #CloudNative #Kubecost

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Azure devs, meet your new best friend: Azure Managed Redis. AMR delivers a fully-managed, scalable in-memory data store for high-performance, reliable, and secure apps, alongside simplified deployment and robust security. Redis Product Manager Purna Mehta guides you through how you can create a new cache using the Azure portal: https://lnkd.in/dM4ZVpDk 

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There’s no one-size-fits-all when it comes to Kubernetes architecture. From public cloud to bare metal, edge to multicloud, each option comes with trade-offs around cost, performance, compliance, and complexity. And in reality, most teams are running Kubernetes across five or more environments today. This blog explores: 🔸 The pros and cons of six common deployment models 🔸 Why organizations are mixing environments to balance risk, cost, and AI demands 🔸 How to think about “what you can choose” vs. “what you can manage” Read the full blog to see how to find the perfect Kubernetes architecture for your own needs, and how Palette can help you manage it all 👉 https://okt.to/nz3G12 #Kubernetes #KubernetesManagement #CloudComputing #EdgeComputing

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Master Kubernetes with LearnKube's Advanced Kubernetes workshop! What should you expect? - Learn how to architect and design clusters from the ground up (in the cloud or on-prem). - Explore the Kubernetes internal component and how the system is designed with resiliency in mind. - Deep-dive into the networking components and observe the packets flowing into the cluster. - Hands-on labs to test the theory with real-world scenarios! - And more. The next course starts in September: https://lnkd.in/ge4aYVZq We also run in-person courses and private training: https://lnkd.in/g9N3m-Xr

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Running APIs in Kubernetes? It’s not as simple as dropping your gateway into a cluster. This post breaks down the real challenges of API management in K8s, from autoscaling and observability to security and GitOps, and shares how Kubernetes-native approaches can help you solve them. A must-read if you’re navigating API complexity in modern environments: 🔗 https://lnkd.in/g8eTaavv #APIManagement #Kubernetes #CloudNative #DevOps #WSO2 #APIGateway

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Monitoring Kubernetes? Here’s your quick hand guide, which includes best practices to keep clusters healthy, key metrics every team should track, how to use AI for accelerating RCA, and choosing the right observability solution for your stack. Whether you’re scaling workloads, troubleshooting performance, or exploring AI, this guide will help you stay ahead. #Kubernetes #Guide #Monitoring #SRE #DevOps

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For organizations navigating the complexities of data sovereignty, this is a significant step forward. Google Cloud is rolling out a portfolio of sovereign cloud solutions specifically for Canada, giving our customers more control than ever. Read the full announcement: https://lnkd.in/gsJPyCqh #GoogleCloud #DataSovereignty #Cloud #Security #Canada #Tech

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Interesting to see AWS creates an Airflow Custom Operator to run Spark on EKS via our open source project Batch Processing Gateway: https://lnkd.in/ghvqn-mb Check out Batch Processing Gateway (https://lnkd.in/gGjVCWWe) to see how you can easily run Spark in a multi-cluster Kubernetes environment.

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𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 1.34: 𝗣𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗿𝗲𝘀𝘁𝗮𝗿𝘁 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲 A long-standing limitation in Kubernetes has been removed. Restart behavior is no longer tied to the Pod level. With Kubernetes 1.34, each container can now have its own restart policy, and rules can be applied to specific exit codes. The new model is far more flexible: • 𝗚𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: Policies like Always, Never, or OnFailure can now be set per container. • 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Exit-code rules allow containers to retry recoverable failures while failing fast on critical errors. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Restarts happen in place, cutting recovery from up to a minute down to seconds, while preserving volumes and networking. This matters in practice. A migration container can now run once and never restart. A machine learning job can retry GPU memory errors without being rescheduled. A microservice with logging and monitoring sidecars can give each one restart logic suited to its role. The feature is still in alpha and gated, but it is worth testing. It reduces complexity, improves reliability, and reflects the way modern workloads actually fail. I break down examples, exit-code design patterns, and migration guidance in the full write-up: https://lnkd.in/enXiUvjU #Kubernetes #CloudNative #DevOps #MLOps #PlatformEngineering

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