ml-platform-workflow

Build ML pipelines you can trust — visually on a canvas or in Python. Self-hosted platform + Apache-2.0 library. Catches data leakage, keeps scores honest, and takes you from raw data to monitored model.

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.

Hie.. i have build the student prediction model using ML algorithms and concepts as we need the student dataset analysis but its hard to perform it manually. My prediction system will work just for you and your dataset.

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.

A lightweight, config-driven ML training framework