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)
Overview of AWS, GCP, and Azure ML services. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 17)
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)
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)
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)
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)
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)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...