MLOps 2.0 Solves Monitoring Challenge

Best MLOps Platforms 2026: MLflow, W&B, Comet Ranked A data-driven ranking of 15+ MLOps platforms across experiment tracking, model registry, deployment, and monitoring - for traditional ML and modern LLM workflows. #Tools #Mlops #WeightsAndBiases https://lnkd.in/dfYekXES

Best MLOps Platforms 2026 Ranked A data-driven ranking of 15+ MLOps platforms across experiment tracking, model registry, deployment, and monitoring - for traditional ML and modern LLM workflows. #Tools #Mlops #WeightsAndBiases https://lnkd.in/dfYekXES

Elastic Launches LLM-Powered Monitor to Detect Real-Time Supply Chain Compromises 📌 Elastic unveils a groundbreaking LLM-powered tool that scans top packages in PyPI and npm in real time, catching supply chain threats before they spread. Using AI to analyze code diffs for obfuscated payloads, persistence, and network anomalies, it auto-alerts teams-outpacing static scanners. Open-source and ready for DevOps, it turns dependency audits into proactive defense. 🔗 Read more: https://lnkd.in/dESswj6N #Elastic #Llm #Pypi #Npm #Supplychain

Your Kubernetes clusters are unlocked. Your CISO doesn't know. Most policy engines are deployed in audit mode — flagging violations, logging them, doing nothing to stop them. By the time Wiz alerts you, the misconfigured workload is already running in production. AI is shipping code 10x faster. Audit mode was inadequate before. It's irrelevant now. 3 questions to ask your platform team today: - What % of our policies are in enforce mode? - When did we last promote a policy to enforce? - Are we catching violations before they reach the cluster? Swipe → we break down the full picture. 👇 Blog post in comments

Your Kubernetes clusters might look secure.. but if most policies are still in audit mode, you're detecting risk instead of preventing it, and in an AI-accelerated world, that gap is where breaches happen.

Datadog is using OpenAI Codex to bring system-level context into code review. Every PR is evaluated beyond the diff: • cross-service interactions • API contract changes with downstream impact • gaps in test coverage across coupled systems To validate it, #Datadog replayed real incidents against historical PRs. → Codex identified issues in ~22% of cases that had already passed review “For me, a Codex comment feels like the smartest engineer I’ve worked with and who has infinite time to find bugs.” - Bradley Carter, Engineering Manager at Datadog What changes in practice: • risks surface earlier, before production • reviewers spend less time hunting for edge cases • more attention goes to design and architecture Read all about it: https://lnkd.in/ew6Xi63G #codex #openai #datadog

A stack trace tells you where an error happened. It doesn't tell you why it was introduced, or who's responsible for fixing it. Port now supports the Sentry MCP Server so developers can query Sentry error data from Port AI alongside service ownership and recent deployments from Port's catalog. Triage faster. Assign correctly. Fix sooner. Learn more about Port’s new MCP Connector: https://lnkd.in/eeXVz3Qn

Fixing bugs isn’t just about seeing the error, it’s about having the right context. Sentry shows what broke. Port.io provides the why and who. With MCP connecting the two, AI can move from error → owner → fix in one flow. Faster triage. Quicker fixes. Less tool-hopping. This is what strong partnerships should look like, meeting developers where they are and helping customers resolve issues faster. Grateful for the partnership with Sentry!

Quick build log from last week. We shipped the Tabulator MCP integration fix. The bug was small but annoying: certain package metadata formats were causing the MCP connection to Tabulator to silently fail. Nobody got an error. The data just didn't show up. Took us longer than I'd like to admit to track down because silent failures are the worst kind. Also reworked how AI model selection works in Curator. You can now pick which Claude version powers your data queries without redeploying. Sounds small but it matters when Anthropic ships a new model and you want to test it on your data the same day. The harder thing I've been thinking about: we pulled an AI-generated caching mechanism out of a feature because it was bypassing real data interactions. The test suite didn't catch it because the cached responses looked correct. They just weren't live. Shipping AI-powered features while policing AI-generated code in your own codebase is a strange place to be. Still working out how to think about that one. Repo is open source if you want to follow along. #LifeSciences #OpenSource #DataManagement

This spring break, I built "Friday" (named after Iron Man's OTHER AI), a production home lab where I practice industry standard skills: container orchestration, CI/CD pipelines, automation, and observability. What it does VM 1: the core services - Self-hosted services (Immich for photos, Nextcloud for files, Vaultwarden for passwords) - Full monitoring stack with Grafana, InfluxDB, and Telegraf to track system metrics VM 2: locally hosted Ollama LLM - phi3:mini allows for AI-powered alerting via an n8n pipeline that sends AI-interpreted incidents to a Discord bot on my phone VM 3: k3s cluster - Hosts my portfolio website, deployed on k3s with a GitHub Actions CI/CD pipeline for edits + Cloudflare tunnel to avoid port forwarding All on a refurbished OptiPlex 3080 Micro running Proxmox. I learned that building an infrastructure works in iterations bringing pieces together instead of throwing them all at once. Breaking VMs and losing track of compose files taught me more than any tutorial - iteration and failure are the best teachers. Read the full breakdown: markcalip.com #DevOps #CloudEngineer #SoftwareEngineering #MLOps #Kubernetes #Docker #CICD #AI #SelfHosted #Homelab #LearnInPublic #CloudComputing