How to deploy ML models on serverless architecture

Ever struggled to keep your ML features consistent between training and production? This article does a great job breaking down what a Feature Store Architecture really is and why it’s such a game-changer for MLOps teams. https://lnkd.in/dkiFwumV

MLOps Lifecycle – Part 2 builds upon the concepts introduced in Part 1 and covers the following key areas: MLOps Lifecycle Use Cases MLOps Architectural Diagram MLOps Lifecycle: Algorithms and Techniques

Numaflow allows engineers without Kubernetes experience to easily build and manage data processing pipelines for high-throughput workloads. By Joab Jackson feat. Sriharsha Yayi

Numaflow allows engineers without Kubernetes experience to easily build and manage data processing pipelines for high-throughput workloads. By Joab Jackson feat. Sriharsha Yayi

New Working Draft Released: XHAL Blueprint v1.1 (Azure + Private Cloud Architecture for HITL AI) After a long day and night (and yes, a few tech hiccups along the way!) I'm proud to share this early working copy of the XHAL Azure & Private Cloud Architecture and Implementation Blueprint — v1.1. 🧠 This isn’t just another architecture doc. It’s the real-world infrastructure and governance blueprint for deploying Human-in-the-Loop (HITL) AI across education, healthcare, and public services — with safeguarding and ethical design baked in. 📌 What’s inside: Layered architecture (Strategy ➝ Implementation ➝ Deployment) XHAL’s proprietary HITL orchestration (XHILOS™) and compliance engine (AADDAN™) Secure Azure + Private Cloud integration with real-time escalation logic Python + Ruby-based AI model pipelines and REST API integrations GitHub CI/CD flows built for scale and observability Tailscale zero-trust networking for multi-cloud privacy DWDM edge connectivity and IoT-ready fallback infrastructure 👀 It’s rough. It’s real. And it’s what we’re building — transparently and collaboratively. 🔁 Whether you're an architect, PM, AI lead, or someone exploring secure AI delivery in sensitive sectors — this doc shows how we’re doing it, layer by layer. 📄 Download the full working draft here and let me know your thoughts 👇 (See attached document — .docx for easy markup.)

Developers be like: “What if we add Kafka, microservices, and AI pipelines?” Reality: You just needed a Google Sheet. 😅 Over-engineering isn’t ambition — it’s procrastination disguised as architecture.

While designing a feature to stream real-time updates via Server-Sent Events (SSE), I conceptualized a class that listens to multiple data sources, Redis channels, async functions, internal services, and re-emits those updates in a standardized event format to clients. Out of curiosity, I used AI to check if there's an existing architectural pattern that aligns with this approach. To my surprise, I discovered the Event Aggregator pattern, introduced by Martin Fowler in Patterns of Enterprise Application Architecture. This pattern consolidates events from multiple sources and delivers them to interested listeners. It’s fascinating how often we use patterns every day without knowing the vocabulary. Recognizing and naming them is what elevates our thinking: “We all write code, but engineers understand the vocabulary of architecture, the shared language that helps us communicate and reason about complex systems.” - Mark Richards Understanding this vocabulary is what differentiates a developer from a software engineer, it helps us design systems more effectively, communicate clearly, and reason about complexity with confidence. #SoftwareEngineering #DesignPatterns #EventDriven #Architecture #ContinuousLearning

Every once in a while, I come across a performance deep dive that answers a question I’ve always wondered about — and just keeps going. This time it’s Ben Chess’s incredible blog post on scaling Kubernetes to 1,000,000 nodes. He digs into every layer of the problem — from running out of IPs to pushing the limits of etcd and API performance — and explains how he overcame each bottleneck. If you love performance engineering, distributed systems, or just great technical storytelling, this one’s worth your time: https://lnkd.in/dAJn9sfw

🚀 Vol. 3 is live! Observability 2.0: Metrics, Traces, and Logs in Harmony 🔍 In the latest volume of the Advanced Cloud-Native Patterns series, I explore how metrics, traces, and logs work together to create truly observable systems - going beyond dashboards and into real understanding. 💡 Key topics inside: 🔹 Monitoring vs Observability - understanding the why, not just the what 🔹 OpenTelemetry architecture and pipelines 🔹 Correlation IDs for full request visibility 🔹 Real-time anomaly detection and auto-remediation 🔹 Designing scalable observability pipelines with Prometheus, Loki, Tempo & Grafana The goal isn’t to collect more data - it’s to connect the right data. 📖 Full article → [Medium link in the first comment 👇] 🧠 Coming next in the series: ✨ Vol. 4 - Advanced Secret Management & Policy Enforcement ✨ Vol. 5 - Self-Healing & Autonomous Infrastructure Each volume dives into high-level, fully technical cloud-native patterns - for engineers, platform teams, and architects who want to go beyond the basics. ⚡ Stay tuned - to be continued... #Kubernetes #DevOps #CloudNative #PlatformEngineering #Observability #OpenTelemetry #Metrics #Traces #Logs #Monitoring #SRE #Infrastructure

We build the production standard for ML teams: pipelines that stand up fast, deploys that roll back safely, and metrics that make decisions obvious. Built for Vertex/Azure ML • PySpark • Airflow • MLflow/Feast • Kubernetes. Built for leaders who need Time-to-value: go live ≤72h Confidence: guardrails for drift, cost, and SLOs Clarity: one place to see lift, latency, and risk Follow for executive-grade diagrams, short demos, and field notes from real pipelines. #mlops #engineering #software #platform