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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)
A/B Testing and Shadow Deployment for ML Models
Ship a New Model Without Betting Everything on It: Shadow It, Canary It, Split Traffic 80/20, and Let Statistics Decide the Winner. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 50)
Week 7 Review: Monitoring and Observability Stack
Run Metrics, Logs, Traces, Alerts, and One ML Dashboard Together in a Single Docker Compose Stack, Then Hunt for the Gaps. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 49)
Introduction to Model Explainability and SHAP
Answer Why Your Model Predicted That: Compute SHAP Values, Read Global and Local Explanations, and Store the Plots With Every Model in MLflow. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 48)
Alerting and On-Call for ML Systems
Turn Your Dashboards Into Alerts That Matter: Prometheus Rules for Latency, Errors, and Low Confidence, Routed Through Alertmanager to Slack. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 47)
Distributed Tracing with OpenTelemetry
Follow One Prediction Request Through Every Service and Step, See Exactly Where the Milliseconds Go, and Link It to Your Logs. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 46)
Structured Logging for ML Services
Replace Print Statements With One JSON Event per Prediction: Inputs, Output, Confidence, Latency, Model Version, and a Request ID You Can Search.
