ML-MLOps-Portfolio

Scaffold and run batch scoring jobs (CronJob + Parquet output) that reuse the service's model + feature-engineering code without opening the live API

Root-cause a performance alert using sliced metrics + ground-truth

Review cloud costs against budget and identify optimization opportunities

Debug ML inference issues — latency spikes, wrong predictions, event loop blocking

Deploy ML service to EKS with Kustomize overlays and IRSA

Deploy ML service to GKE with Kustomize overlays and Workload Identity

Run and interpret DATA drift (PSI) AND CONCEPT drift (sliced performance) for an ML service

Run 6-phase exploratory data analysis on a new dataset — ingest, profile, univariate, correlations, leakage gate, feature proposals

Execute model retraining with quality gates and safe promotion

Create a complete new ML service from template — end-to-end scaffolding

End-to-end RCA for a performance-degradation incident — correlates sliced metrics, drift, deploy history, upstream data changes, and prediction logs into one evidence-backed root cause.

Full release checklist for multi-cloud deployment (GCP + AWS)

Emergency rollback procedure for production ML service — Argo Rollouts abort + kubectl undo + MLflow revert + alert silencing

Automated scan of a service/repo for compliance with AGENTS.md invariants D-01 through D-27 — produces a PASS/FAIL report with file:line evidence

Incident playbook for leaked secrets — detect, revoke, rotate, audit access, notify, post-mortem

Pre-build and pre-deploy security audit — secret scans, IAM least-privilege, IRSA/WI verification, image signing, SBOM