Building infra that heals itself. Teaching machines to do the same.
| ποΈ AIOps / SRE | Large-scale Kubernetes infra, GitOps pipelines, ML-powered auto-scaling & intelligent alerting |
| π€ MLOps / AI | End-to-end ML pipelines, model deployment, drift detection, AI-driven infra decisions |
| β‘ GPU / CUDA | GPU-accelerated computing, CUDA programming, NVIDIA profiling & deep learning on GPU |
| Area | Topics |
|---|---|
| π AIOps | Anomaly Detection Β· Root Cause Analysis Β· Predictive Alerting Β· Self-Healing Infra |
| π MLOps | Model CI/CD Β· Feature Stores Β· Model Monitoring Β· Drift Detection Β· Kubeflow |
| π€ Agentic AI | MCP (Model Context Protocol) Β· A2A (Agent-to-Agent) Β· Agentic AI Β· LLMOps Β· AI Agents |
| β Platform Eng | GitOps Β· ArgoCD Β· Service Mesh Β· Traefik Β· Zero-Trust Infra |
| π Observability | OpenTelemetry Β· Prometheus Β· Grafana Β· ELK Β· Jaeger Β· Traefik Β· Distributed Tracing |
| β‘ GPU / CUDA | CUDA C Β· Nsight Systems Β· Nsight Compute Β· Parallel Computing Β· GPU for ML/DL |
| π§ ML / AI | Regression Β· Clustering Β· Parameter Estimation Β· Deep Learning Β· Vector DBs |
| π Security | Vault Β· Kyverno Β· mTLS Β· RBAC Β· Policy as Code |
LLMOps MCP β Model Context Protocol A2A β Agent-to-Agent Agentic AI AI Agents ML on Kubernetes Random Forest for Auto-scaling Kubeflow Vector Databases Reinforcement Learning

