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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.

Struggling to learn data science alone? This article explores the value of study partnerships and pairs them with the classic Hands-On ML textbook to offer a phased learning plan from math foundations to deep learning.

A firsthand account shared on Reddit reveals what a machine learning engineer online assessment (OA) at a top US tech company is really like. This article breaks down OA modules, role differences, and prep strategies for FAANG job seekers.

Should full-stack developers learn machine learning? This article analyzes the difference between applied ML and research ML, breaks down the ROI at each stage, and offers a concrete action path.
The Documentation Dilemma: Why Enterpr…
From retrieval difficulties to lagging updates and disconnected workflows, three dilemmas plague traditional documentation. Explore how the AI era can break the deadlock and get knowledge flowing.

An in-depth analysis of the zero-dependency decision record auditor: from AI compliance and incident postmortems to human-AI accountability, exploring how 'Governance as Code' enables traceable, transparent AI decision-making.
Local Coding Agents in Practice: A Com…
An in-depth look at local coding agents—core concepts, advantages, and real challenges. Compare against Claude Code and learn to build a zero-subscription, private AI coding workflow with open-weight models.

A Power Platform MVP demonstrates how to use MCP to securely expose Power Apps business data to M365 Copilot. Covers declarative agent creation, custom tool development, and VS Code setup.

July 12 GitHub trending: Agent Skills/MCP ecosystem explodes with superpowers hitting ~900 stars, pgrust rewrites Postgres in Rust passing 100% tests, plus solid engineering foundations.
Devin Integrates GPT-5.6: A Dual Break…
Devin integrates GPT-5.6, achieving top-tier coding agent performance and exceptional token cost efficiency. Explore how this reshapes the AI coding ecosystem.

Frugon is an MIT-licensed, local LLM cost analysis tool that helps developers identify which API calls can be switched to cheaper models for data-driven cost reduction — no log uploads, full privacy.

Learn how to connect SSMS SQL projects to an Azure DevOps CI/CD pipeline—covering YAML build config, SQL code quality analysis, managed identity authentication, and dynamic firewall rules for secure Azure SQL deployment.

A 6-year electrical engineer from Brazil weighs transitioning to AI engineering. This deep-dive covers the stability vs. freedom tradeoff, transition advantages, and a practical roadmap for engineers with similar backgrounds.

Step-by-step guide to deploying Dify locally: Docker setup, Docker Compose installation, source code configuration, .env file setup, and container startup for Windows, macOS, and Linux.

A deep dive into Databricks Agent Framework (Mosaic AI): unify LangGraph/OpenAI agents via ChatAgent, log & evaluate with MLflow, version with Unity Catalog, and deploy Model Serving Endpoints for production AI agents.

Databricks open-sources Omnigent, a Meta-Harness for orchestrating Claude Code, Codex, and more AI coding assistants together—with built-in guardrails, cross-model workflows, and real-time collaboration. Get started in 10 minutes.

An in-depth look at the Skills paradigm in AI programming: through intent routing and script encapsulation, let AI agents auto-manage multi-channel LLM APIs on a One API gateway for one-click distribution, health checks, and auto-degradation.

Learn automation testing from scratch! This article breaks down a three-stage path: Selenium/Appium tools, Requests+PyTest API testing, performance testing and CI/CD, with real projects—build a complete skill set in 21 days.

An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.

Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.