SURVIVE THE AI ERA as a Cloud & DevOps Engineer
AI is not replacing engineers. It is replacing engineers who don't evolve
AI is not replacing engineers. It is replacing engineers who don't evolve
From Beginner to Expert for Cloud & DevOps Engineers
From Beginner to Expert for Cloud & DevOps Engineers
From Beginner to Expert for Cloud & DevOps Engineers
Use these eBooks to deepen your understanding of Kubernetes, Terraform, and Freelancing, and accelerate your job hunt with the included Job Search Tracker (Exclusive for LearnXops Premium members)
Containerize your ML code so it runs identically everywhere — on your laptop, on a teammate's machine, and in production — with zero "works on my machine" surprises. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 11)
Systematic search strategies, MLflow-tracked runs, and the art of comparing hundreds of experiments to extract your best-performing model. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 10)
Stop running ML code in isolation. MLflow Projects make your experiments reproducible on any machine — while the Model Registry gives your models a professional lifecycle: from experiment to staging to production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 9)
Stop losing track of your best model. Learn how MLflow turns chaotic trial-and-error into a reproducible, queryable history of every experiment you've ever run. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 8)
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 7)
Stop running scripts manually. Learn how to define reproducible, dependency-aware ML pipelines using DVC's declarative dvc.yaml, track every run automatically, and share exact pipeline states with your team — no Makefile required. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 6)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...