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Learning resources for “mlops”
Pulled from roadmap.sh, GitHub, freeCodeCamp, YouTube & Dev.to
Mlops — Official Roadmap
Step-by-step visual learning path covering all skills, tools, and concepts for mlops.
mlops Full Course & Tutorials
Browse thousands of free mlops courses, tutorials and roadmap walkthroughs.
Learn mlops — freeCodeCamp
Free, project-based mlops courses and certifications with no ads.
musamaanjum/ai-engineer-roadmap
A complete roadmap to become an AI/LLM Engineer in 2026. Free & paid courses, 5 portfolio projects, interview prep, and resources. For software engineers with Python & ML basics who want to break into AI. Covers Transformers, RAG, fine-tuning, agents, LangChain, MLOps, and deployment.
dhirajxai/ai-career-transition-roadmap
Practical roadmap for switching into AI from software, data, product, QA, ops, design, or non-technical roles.
akshitsutharr/Data-Science-Roadmap
A complete Data Science Roadmap — from coding, math & stats to ML, DL, deployment, and projects. Beginner-friendly, practical, and curated to help you build skills, portfolio, and confidence.
djordjeperovic/python-ds-ml-roadmap
A structured, hands-on learning path from Python basics to production ML — notebooks, projects & cheat sheets
MLOps Lifecycle: Stages, Workflow, and Best Practices
Understand the MLOps lifecycle from data preparation to deployment, monitoring, governance, and retraining.
I Revived a Broken MLOps Platform — Now It's Self-Service, Policy-Guarded, and Operationally Credible
I abandoned this Kubernetes platform on April 4th. 48 days later I rebuilt it: CrashLoopBackOff everywhere → self-service GitOps, policy enforcement, and deterministic recovery. 21 checks. 0 failures. Here's exactly how GitHub Copilot helped.
AI Experimentation Best Practices: From Evaluation to Safe Production Rollouts
Learn how to evaluate, experiment with, and safely roll out AI changes using metrics, guardrails, AgentControl configs, online evaluations, and LaunchDarkly release controls.
We shipped a model on a 2-point eval win. It was noise.
TL;DR: We promoted a fine-tuned 7B because it beat the incumbent by 2.1 points on our internal eval....