SURVIVE THE AI ERA as a Cloud & DevOps Engineer
AI is not replacing engineers. It is replacing engineers who don't evolve
LearnXops - Learn DevOps, DevSecOps, CloudOps, MLOps, AIOps ....XOps easy way, no fluff — the newsletter to grow your EverythingXOps knowledge
Join 10,000+ Cloud, DevOps, MLOps ...XOps professionals getting real-world use cases, and free high-quality learning resources — every week.
Join 10,000+ Cloud, DevOps, MLOps ...XOps professionals getting real-world use cases, and free high-quality learning resources — every week.
Get the email newsletter and unlock access to members-only content and updates
Processing your application
Please check your inbox and click the link to confirm your subscription.
There was an error sending the email
Get started with our best stories
Kubernetes Production Guide on AWS
From Beginner to Expert for Cloud & DevOps Engineers
Shell Scripting Handbook
From Beginner to Expert for Cloud & DevOps Engineers
Linux Master Handbook
From Beginner to Expert for Cloud & DevOps Engineers
Ultimate DevOps Bundle: Kubernetes Handbook, Terraform Masterbook, Freelancing Masterbook & Job Search Toolkit
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)
Serving Models with MLflow Models Serve
Move from experimentation to production by spinning up a REST API for your trained model with a single MLflow command. Learn mlflow models serve, model signatures, input schemas, and how to send real-time predictions. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 16)
Model Serving Fundamentals & REST APIs
A trained model sitting in a .pkl file helps nobody. Today your iris classifier gets a front door: a REST API that any application, in any language, can call over HTTP. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 15)
Review Week 2 & The Containerized ML Experiment Workflow
Two weeks in, you have all the pieces: Git and DVC for versioning, MLflow for tracking, and Docker for reproducible environments. A single docker compose up that spins up a tracking server, runs a versioned experiment, and logs every result. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 14)
Docker Compose for Multi-Service ML Environments
Stop running MLflow and your training script in separate terminals. Today you'll wire them together with Docker Compose , one command, a full local ML platform, ready to log every experiment automatically. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 13)
Writing Production-Grade Dockerfiles for ML
Multi-stage builds, .dockerignore hygiene, layer caching, and dependency pinning , the engineering discipline that separates a "it works" image from one you'd actually ship to production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 12)
Docker for ML Engineers
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)
