#DevOps monitoring
122 related articles
·3 min
What Is CI/CD? A Complete Guide to Automated Release Pipelines at Tech Companies
CI/CD is the automated release pipeline powering modern software. Learn how continuous integration and deployment work, their core value, and where they can fail.
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·3 min
Getting Started with Jenkins: The Core Hub of CI/CD Automation Explained
A beginner's guide to Jenkins: its role as the CI/CD and DevOps hub, JDK requirements, and the four plugin categories (source control, build, security scanning, deployment) that power automation pipelines.
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·4 min
Understanding CI/CD from Scratch: Core Principles and Practical Introduction to Automated Deployment
A clear, practical guide to CI/CD: from Waterfall to DevOps, manual vs. automated deployment, and a full Jenkins + RuoYi hands-on learning path for beginners.
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·3 min
MLOps from Scratch: A 100-Day Learning Challenge Roadmap and Practical Guide
A college student's MLOps 100-day challenge documents the full journey from Python engineering and Git to Docker, model deployment, and monitoring. A practical roadmap for data scientists transitioning to ML engineering.
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·2 min
23K Stars on GitHub: The Hackathon Champion's Ultimate Claude Code Configuration Guide
A Claude Code open-source config with 278 skills and 67 sub-agents helps developers ship a full MVP in 8 hours. Covers security scanning, silent failure detection, and experience migration. Free under MIT license, compatible with Cursor and Codex.
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·2 min
Build From Scratch or Just Use sklearn? A Complete ML Engineer Learning Path Guide
Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.
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Tailscale SSH Critical Vulnerability T…
·2 min
Tailscale SSH Critical Vulnerability TS-2026-009: Restricted Users Can Escalate to Root
Tailscale discloses critical vulnerability TS-2026-009 in its built-in SSH feature. Insecure argument handling lets restricted users bypass ACLs and gain root. Learn the technical details and how to respond.
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·2 min
AI Task Routing for Cost Reduction: A Framework for Smaller Teams to Evaluate ROI
Task routing is hailed as a silver bullet for LLM cost reduction, but routing strategy design, model training, and self-hosting each carry hidden engineering costs. This deep dive helps smaller teams evaluate ROI and offers a phased implementation path.
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·3 min
Agent Performance Evaluation: A Three-Layer Framework and Engineering Implementation Guide
A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.
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·2 min
LLM Red Teaming: One-Time Snapshot or Continuous Adversarial Testing? A Complete Guide
Every prompt or RAG change in an LLM app can reintroduce security flaws. This guide explains why traditional pentest logic fails on LLMs and how to build change-driven continuous adversarial testing.
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Production-Grade LangGraph Template: A…
·2 min
Production-Grade LangGraph Template: A Practical Guide to Engineering AI Agents for Production
A deep dive into production-grade LangGraph templates covering state management, observability, error handling, and containerized deployment to bridge the gap from demo to production.
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·3 min
Is Your Training Job Quietly Slowing Down? TraceML Exposes PyTorch's Hidden Performance Bottlenecks
GPU at 51% utilization — and no one noticed? See how TraceML exposes hidden PyTorch DataLoader bottlenecks, cuts training time 43% with 3 parameter changes.
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·4 min
LangChain in Practice: Mastering the Four Core Modules of the Agent Ecosystem
A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.
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Computer Vision Career Paths: A Guide …
·3 min
Computer Vision Career Paths: A Guide for Undergraduates
Is Computer Vision worth pursuing as a career? This guide covers CV job market realities, master's vs. industry tradeoffs, edge deployment skills, and how to transition toward multimodal AI engineering.
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After Getting Started with AI/ML: Shou…
·3 min
After Getting Started with AI/ML: Should You Pursue an Internship or Continue Studying?
Already trained models and implemented neural nets from scratch — should you apply for internships or keep studying? A practical guide to entry-level AI roles and how to advance.
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The Complete AI Researcher Learning Ro…
·3 min
The Complete AI Researcher Learning Roadmap: A Beginner's Guide to Deep Learning
A structured AI/ML learning roadmap covering Python, math, machine learning, deep learning, and MLOps — with timelines, milestones, and free resource recommendations.
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·2 min
Learning MLOps from Scratch: A Deep Dive into the 24-Month Roadmap
Can you learn MLOps from scratch? This guide breaks down core skill requirements and offers a practical 4-phase, 24-month roadmap covering Python, ML, DevOps, and MLflow.
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·4 min
The Complete Roadmap from DevOps to MLOps: Skill Transfer and Toolchain Guide
How can DevOps engineers transition to MLOps? This guide explains the core differences between MLOps and DevOps, offers a phased learning path, tool recommendations (MLflow, DVC, Kubeflow), and practical project ideas.
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·3 min
How Machine Learning Engineers Can Adapt in the AI Era: A Guide to Skill Trade-offs and Career Reinvention
In the AI wave, ML engineers' work is quietly shifting: from building models to using them, from feature engineering to LLM app development. This article outlines the new skills to prioritize, fading old ones, and how to turn AI into career leverage.
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·3 min
Career Switch to AI Engineer Without a CS Degree: A Self-Study Roadmap from Scratch
A real case study of an agriculture student breaking into AI: how to start with CS50 and systematically master Python, machine learning, and MLOps skills, with a three-phase transition plan for self-learners.
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