Build and ship production ML pipelines faster: a pipeline library with an optional self-hosted visual layer for modular, reproducible workflows, local testing, and experiment tracking.

An AI-powered cloud threat detection system with a full MLOps lifecycle multi-source log ingestion, unsupervised anomaly detection, MITRE ATT&CK mapping, CVE enrichment, and a Claude-powered SOC analyst, all wired into a Kubeflow pipeline that trains, gates, and deploys to KServe automatically.

The project combines traditional quantum computing and machine learning techniques in novel ways using: Quantum algorithm simulation: It offers applications for Shore, Grover, and quantum Fourier transform (QFT) algorithms, making it an environment for testing these algorithms with the impact of holographic shielding techniques.

Shared development toolbox for engineers. Provides reusable data + modeling pipelines and a unified packaging/deployment client for ml-deployment-ecosystem. Not for storage of models/data or high-frequency production extraction.

Small on-prem machine learning ecosystem for small-data environments, where fast iteration, maintainability, and reliable deployment matter more than large-scale infrastructure.

A comprehensive Deep Learning-based Heart Disease Prediction System that analyzes patient clinical data and predicts cardiovascular disease multi-class risk classification (Low, Medium, High Risk) through an Artificial Neural Network (ANN) and binary disease detection via Random Forest and Logistic Regression models.