Murtaza Doctor

🧠 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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Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

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💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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For industry leaders like RS, API orchestration is the foundation for efficiency and scale. With Apollo’s graph-based orchestration platform, they unlocked 10x performance, and reduced latency by 24%, enabling their teams to build and innovate across previously siloed systems. See how RS modernized its tech stack to support future growth → https://lnkd.in/gYKdmGCG #GraphQL #APIorchestration #ApolloChampions

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Cribl Stream helps you control how data flows. Imply Lumi helps you control how data is stored and searched. Together, they deliver a modern observability architecture built for control, performance, and efficiency: - Route and retain what matters most - Search quickly and interactively across all retained data - Integrate seamlessly with your existing stack - Reduce costs without disrupting workflows Lumi isn’t just a faster backend — it’s part of a next-generation architecture that decouples storage, indexing, and query from the observability tools above. That means greater flexibility, less lock-in, and a solid foundation for what comes next. https://bit.ly/49iAXm1

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People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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Two of AWS’ Sr. Principal Engineers, Nicholas Matsakis and Marc Bowes, take us inside the development of Aurora DSQL in a fascinating new blog. The post shows how the team scaled write operations without two-phase commit, overcame garbage collection hurdles, and ultimately embraced Rust for both data and control planes. What I find particularly interesting is how the team's journey with Rust evolved. They started cautiously, using it for a single component, and ended up rewriting almost everything in Rust. It's a reminder that sometimes the hard choices - like adopting a new language - can unlock tremendous value. The full post is well worth your time. It's not just about database internals, but also about team dynamics, a culture of learning (and questioning established approaches), and making difficult architectural decisions. Read it here: https://lnkd.in/eMhxBu9e Now, go build!

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🎯 Running inference workloads on Kubernetes? I just wrote a deep dive on Gateway API Inference Extension and how it works. The Gateway API Inference Extension introduces two new Custom Resource Definitions (CRDs): InferenceModel and InferencePool. By using these two new resources, along with the endpoint selector, your L7 routing infrastructure becomes an “Inference Gateway” enabling you to self-host GenAI/LLMs with a “model-as-a-service” mindset. https://lnkd.in/g_eFqwUd #ai #llm #genai #inference

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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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From the start, Metronome knew real-time billing couldn’t be built on batch. That’s why they chose Confluent as the foundation for their data architecture, streaming billions of events daily and processing tens of thousands of invoices per second with ease. We handle the infrastructure complexity so 90% of their engineers can focus on building and improving the product. Explore how they’re delivering a modern billing experience powered by real-time data infrastructure → https://cnfl.io/40S0YDS

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Kubernetes cost optimization is more than just monitoring - it’s a strategy. This article compares Kubecost and OpenCost, helping you choose the right fit for your enterprise cloud setup. A must-read for DevOps, FinOps, and platform teams 🔗 Read here: https://lnkd.in/ddKzbkTe #Kubernetes #FinOps #DevOps #CloudCost #PlatformEngineering #PlatformEngineers

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Crashes cost revenue. Poor #UX costs trust. #MobileTeams can't afford reactive workflows anymore, and your app is only as good as what your users experience. 😬 CIOReview spotlights how leading teams can detect, prioritize, and resolve issues with our AI-powered #MobileObservability before users ever notice.🛠️ 👉 Get the full insights: https://hubs.ly/Q03p4jYf0

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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Keeping cloud architecture diagrams updated across a fast moving environment can feel like an endless game of catch up. With Lucid’s Cloud Accelerator, teams can create reusable assets for frequently used elements like VPCs, databases, and subnets. Update once and watch changes cascade across every diagram, saving time, reducing manual effort, and keeping documentation consistent as your cloud environment evolves. See how you can bring clarity to your cloud journey: https://lnkd.in/gWyKa5tH

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PagerDuty is now a Cursor customer. Over 30,000 customers rely on PagerDuty to respond to incidents faster, reduce outage risks, and build operational resilience. We’re excited that PagerDuty engineers will now use Cursor’s agents to build these mission-critical products.

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🚀 Big milestone for the StarTree team StarTree Cloud is now serving 1B+ analytical queries every day in production. Even better—90%+ of these queries complete in sub-second latency. This powers mission-critical use cases like: • Fraud detection • Auditing & compliance • Clickstream analytics • Real-time personalization …across FinTech, logistics & delivery, e-commerce, healthcare, and consumer products. What took Snowflake nearly a decade, we achieved in ~6 years—made possible by Apache Pinot, purpose-built to drive down the cost of analytics at massive QPS. Proud of the team and the community that made this possible 🙌 Kishore Gopalakrishna Uday Kiran Vallamsetty Mayank S. Neha Pawar Xiaotian (Jackie) Jiang Xiang Fu Amol Mathur and others 👉 Read more: https://lnkd.in/ga5cac6b

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