Business of DevOps
Three Strategies for Winning the AI Race With DevOps
Marty Puranik | | AI deployment speed, AI DevOps, AI in DevOps pipelines, AI observability, AI pipeline efficiency, AI resource management, AI-driven DevOps strategies, cost-efficient GPU hosting, devops automation, distributed training, faster model training, GPU hosting for AI, GPU infrastructure, HPC for AI, MLOps optimization, model optimization, scalable AI workflowsAI is transforming DevOps. Learn how faster model training, optimized pipelines and smarter GPU infrastructure help teams deliver reliable, scalable AI workflows ...
AI Agent Performance Testing in the DevOps Pipeline: Orchestrating Load, Latency and Token Level Monitoring
Sudhakar Reddy Narra | | AI agent debugging, AI agent load testing, AI agent performance testing, AI CI/CD, AI DevOps, AI observability, AI orchestration metrics, AI-specific alerts, context utilization monitoring, context window saturation, cost-per-interaction, first token latency, inference tracing, LLM cost control, LLM cost optimization, LLM integration testing, LLM token throughput, model orchestration performance, production AI reliability, synthetic conversation testing, token analytics, token-rate monitoring, TPOT, TTFTTraditional testing misses token and context failures. Discover how to measure, test and scale AI agents reliably in production ...
MCP — A Protocol for SREs
Jaimin Jashwantlal Gandhi | | agentic AI in financial services, AI agents, AI observability, AI workflows, AI-powered DevOps, Azure MCP server, building MCP servers, DevOps AI tools, Docker MCP server, infrastructure as code AI, integrated development environment AI, LLM context management, LLM interoperability, MCP, MCP architecture, MCP servers, Model Context Protocol, remediation automation, SRE automationThe Model Context Protocol (MCP) standardizes how AI agents access tools, APIs and data. Learn how SREs can leverage MCP to build smarter, automated workflows ...
Beyond the Cloud: A DevOps Blueprint for High-Performance Bare Metal Infrastructure
Ethan Vance | | bare metal servers, CI/CD build runners, DDoS protected hosting, dedicated servers for databases, DevOps infrastructure, ECC memory, hardware reliability RAID, high-performance computing, I/O intensive workloads, infrastructure as code DevOps, Kubernetes bare metal nodes, low-latency infrastructure, network redundancy, NVMe storage performance, Tier-1 bandwidth providersBare metal servers deliver predictable, high-performance compute, storage, and networking—ideal for databases, CI/CD runners, and Kubernetes workloads ...
Terraform Users: Day 2 Operations Aren’t Failing Over Tools, Because Day 1 Never Happened
Eran Bibi | | cloud drift detection, Cloud Governance, cloud security posture, cloud system of record, Day 1 cloud visibility, day 2 operations, FinOps failure, IaC adoption, IaC coverage, infrastructure discovery, production visibility, shadow IT cloud, Terraform drift, unmanaged resourcesYour disaster recovery plan just failed — not because your DR strategy was wrong — but because you had no idea what you were actually running in production. Every Day 2 tools you've bought (think: FinOps, drift detection, ...
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
Muhammad Yawar Malik | | adaptive systems, AI observability, AI reliability, concept drift, data pipeline reliability, feedback loops, incident response, learning systems, ML operations, MLOps, model drift, model performance monitoring, reliability engineering, site reliability engineering in AI, SREAs AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows, guardrails and cross-disciplinary practices are redefining ...
MyDecisive Open Sources Platform for Processing OpenTelemetry Data
Mike Vizard | | CNCF, DevOps observability costs, golden signals, Kubernetes telemetry, memory-based filtering, MTTR reduction, observability, open-source observability, OpenTelemetry, OTel collectors, proactive monitoring., telemetry filtering, telemetry overloadBy open-sourcing its Smart Telemetry Hub, MyDecisive pushes for an evolution of OpenTelemetry—adding local, memory-based filtering to shrink telemetry volume and help DevOps teams lower observability costs and improve MTTR ...
The Deterministic Future of AI-Generated Code
Yechezkel Rabinovich | | AI code generation, AI code review, AI development workflows, automated pull requests, behavioral verification, CI pipeline, code validation, deterministic guardrails, DevOps AI, ebpf, generated code risks, observability, smarter linters, software reliabilityAI has eliminated the bottleneck of writing code—but introduced massive uncertainty in verifying it. This piece explores why deterministic guardrails, smarter linters, and eBPF-driven observability are becoming essential to code review and ...
Observability is the Next Frontier of DevOps and Cloud Security
Joe Selvam | | adaptive baselines, API tracing, business impact monitoring, Cloud Security, cloud-native, configuration drift, DevOps visibility, devsecops, hybrid cloud resilience, mttr, observability, proactive observability, SRE, unified dashboards, unified telemetryIn today’s cloud-native, hybrid-multi-cloud world, DevOps teams face a new paradox. They can deploy code faster than ever, but their visibility often lags. Traditional monitoring tools might reveal that something broke, but ...
The MLSecOps Era: Why DevOps Teams Must Care about Prompt Security
Alex Vakulov | | AI attack surface, AI chaos testing, AI Governance, AI supply chain risk, autonomous agent security., CI/CD pipeline security, CodeShield, DevOps AI, ISO 42001, LlamaFirewall, LLM security, MLSecOps, model drift, NIST AI RMF, prompt injection, prompt validation, PromptGuard, PromptOps, RBAC for AI, red team AI, runtime monitoring, secure AI pipelinesAI-driven software delivery introduces new risks, especially prompt manipulation within CI/CD workflows. This article details the emerging fields of PromptOps and MLSecOps and offers practical strategies for securing prompts, models, and pipelines ...
Google Code Wiki Aims to Solve Documentation’s Oldest Problem
Tom Smith | | AI code analysis, AI documentation tools, automated architecture diagrams, automated code documentation, code repository documentation, codebase understanding, developer productivity tools, legacy code maintenance, onboarding developers faster, software architecture diagramsGoogle introduces Code Wiki, an AI-powered platform that automatically generates and maintains documentation for code repositories. With Gemini integration, live diagrams, and interactive code-aware chat, Code Wiki aims to reduce onboarding time, ...
Survey Sees AI Coding Creating Need for More Software Engineers
Mike Vizard | | agentic AI, AI compliance challenges, AI in DevOps, AI in software development, AI tools adoption, AI-generated code vulnerabilities, developer upskilling, DevOps productivity, DevSecOps challenges, GitLab DevSecOps survey, platform engineering, SDLC automation, software engineer demand, technical debt from AI, vibecoding risksA GitLab survey of 3,266 DevSecOps professionals shows AI is boosting code creation but increasing the need for skilled engineers, compliance challenges and human oversight ...
