Articles

DevOps Pipelines, CI/CD, and Automation

What Yocto Teams Learn the Hard Way About Release Process

Getting a Yocto image to boot is only the beginning. The real challenge starts when you have to release, maintain and update that firmware over the next few years. Many embedded teams discover this only after their first production release. This article looks at the common release engineering mistakes that create unnecessary rework and explains how better versioning, reproducible builds, BSP management and release practices can help deliver more reliable Yocto-based products.

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Software materials

Technical Due Diligence for Startups: What Investors Check and How to Pass

Technical due diligence is no longer a simple code review. Investors now assess software delivery maturity, security controls, infrastructure scalability, observability, documentation quality and operational risk before funding decisions are made. This article explains exactly what investors evaluate, why startups fail due diligence, and how to prepare successfully.

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Embedded Linux DevOps

Embedded Linux DevOps vs Cloud DevOps: Why Most Teams Are Still Years Behind

Embedded Linux DevOps continues to lag behind cloud DevOps in many organisations. Long Yocto build times, fragile OTA updates, certification challenges, and manual compliance processes often slow product delivery. This article explores how platform engineering, release engineering, DevSecOps, SBOM automation, and modern CI/CD practices help embedded Linux teams improve delivery speed, security, compliance readiness, and operational reliability.

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CI documentation

MLOps Is Not DevOps With a GPU: The Operational Differences That Break AI Teams

Teams that treat MLOps as “DevOps with a GPU” often spend months discovering why traditional CI/CD pipelines fail under production AI workloads. Model drift, training-serving skew, non-deterministic failures, and data lineage gaps create operational problems standard DevOps systems were never designed to manage. This article explains the core operational differences between DevOps and MLOps, why production ML systems behave differently, and what mature MLOps practices actually look like inside AI-first engineering organisations.

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