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demml / opsml

Sponsor
Star 37

AI lifecycle platform for classical ML and agentic systems. Versioned, encrypted registries for data, models, experiments, prompts, agents, MCPs, and skills. Pre/deploy/post-deployment patterns. Real-time monitoring and evaluation via Scouter. Rust core, governed by design

rust open-source data-science machine-learning ai monitoring deep-learning python3 artificial-intelligence data-engineering governance model-deployment mlops ml-engineering ml-platform generative-ai agentic llm-ops ml-platform-infra
  • Updated May 16, 2026
  • Rust

AmirhosseinHonardoust / Noise-Injection-Techniques

Star 22

Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.

data-science machine-learning deep-learning tabular-data pytorch dropout neural-networks regularization data-augmentation data-quality noise-injection label-smoothing mixup ai-research gaussian-noise ml-engineering model-robustness adversarial-ml ml-theory ml-robustness
  • Updated Nov 15, 2025

AmirhosseinHonardoust / Missing-Data-Doctor

Star 20

Missing Data Doctor is a diagnostic and treatment toolkit for missing values in machine learning datasets. It profiles missingness patterns, visualizes gaps, applies multiple imputation strategies, and evaluates their impact on model performance. Includes automated plots, metrics, and a full HTML report.

python data-science machine-learning automation scikit-learn tabular-data eda pandas data-visualization imputation missing-data data-preprocessing feature-engineering data-pipelines data-cleaning data-quality data-profiling ml-engineering data-reporting ml-workflows
  • Updated Nov 15, 2025
  • Python

AmirhosseinHonardoust / The-Psychology-of-Fraud

Star 18

A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.

data-science machine-learning risk-analysis credit-card-fraud fintech cybersecurity psychology crime-analysis human-behavior fraud-detection anomaly-detection transaction-data digital-identity explainable-ai ml-engineering financial-crime risk-scoring behavioral-analytics behavioral-science fraud-modeling
  • Updated Dec 4, 2025

AKHIL-SAURABH / HealthAI-Project

Star 13

AI-powered medical imaging system for multi-disease chest X-ray detection,built with EfficientNet deep learning, a FastAPI backend, and an interactive Streamlit dashboard. Deployed on Render for real-time healthcare diagnostics, detecting conditions like Atelectasis, Edema and more.An end-to-end project demonstrating model training,API development.

computer-vision deep-learning tensorflow render medical-imaging cloud-deployment model-deployment python-project chest-xray fastapi ml-engineering streamlit ai-engineering end-to-end-ml-project end-to-end-ml
  • Updated May 21, 2026
  • Jupyter Notebook

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