👋 About Me I’m a Platform Engineer with a passion for Kubernetes, Helm, Docker, and Cloud Security. This portfolio documents my journey into MLOps (Machine Learning Operations) and AIOps (AI for IT Operations), combining DevOps, security, and AI to build scalable, secure, and intelligent platforms.

🎯 Career Roadmap Here’s my learning & project roadmap, broken into phases:

1.	Foundations (0–3m) → Kubernetes, Helm, Docker, Security (CKA, CKS).
2.	AIOps (3–6m) → Observability, anomaly detection, predictive autoscaling.
3.	ML Fundamentals (6–9m) → Python, scikit-learn, Andrew Ng specialization.
4.	MLOps (9–15m) → MLflow, Kubeflow, CI/CD for ML models.
5.	Production-Grade MLOps (15–24m) → Scaling with Ray, secure ML pipelines.
6.	Leadership (24m+) → Architecting AI-driven platforms, OSS contributions.

🚀 Highlight Projects 🔹 Kubernetes & Security • Secure Helm Deployments – Helm chart with Pod Security & RBAC. • Container Hardening – Best practices for Dockerfiles. 🔹 AIOps • Observability Stack – Prometheus + Grafana + Loki + Jaeger. • Log Anomaly Detection – Detecting unusual logs with ML. 🔹 MLOps • MLflow Tracking – Experiment tracking + model registry. • Kubeflow Pipeline – End-to-end ML workflow on K8s. • CI/CD for ML Models – Automated ML model training & deployment. 🔹 Advanced • Distributed ML with Ray – Scale ML across nodes. • AI-driven Incident Response – Predict outages & auto-remediate.

🛠️ Tech Stack • Containers & Orchestration → Kubernetes, Helm, Docker • Observability & AIOps → Prometheus, Grafana, Loki, Jaeger, KEDA • MLOps Tools → MLflow, Kubeflow, Airflow, BentoML, Ray • Security → Trivy, Falco, OPA/Gatekeeper, Cosign • Programming → Python (pandas, scikit-learn), FastAPI

📚 Resources • CKA & CKS Prep – KodeKloud • Machine Learning Specialization – Coursera • MLOps Specialization – Coursera • Prometheus & Grafana – Udemy

📊 Progress Tracker • ✅ CKA Certified • ✅ Docker Security Lab Completed • 🔄 Learning AIOps (Prometheus + Grafana + anomaly detection) • ⏳ Next: ML Fundamentals (Python + scikit-learn)

🤝 Contributions & Future Work • Planning to contribute to Kubeflow and MLflow OSS projects. • Building advanced multi-cloud MLOps demo across AWS + GCP.

✨ This portfolio is a living document — I’ll keep updating it as I progress on my MLOps & AIOps journey.