Found 14 repositories(showing 14)
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sanikasalunke
A modular ML pipeline built with Python, scikit-learn, and Docker, featuring YAML-based config management, DVC tracking, CI/CD integration via GitHub Actions, and production-ready FastAPI deployment. Designed for reproducibility, scalability, and monitoring readiness (Prometheus/Grafana).
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A Udacity project para deploy um ML usando fastapi
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hammadwaheed1133
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GritP
Final project "Deploying a ML Model to Cloud Application Platform with FastAPI" of the Udacity course "Deploying a Scalable ML Pipeline in Production".
Project 3 of udacity Machine Learning DevOps Engineer Deploying a Scalable ML Pipeline in Production-on-Heroku-with-FastAPI.
Aayush2302
A production-grade end-to-end MLOps project for detecting phishing websites using real-world security features. Includes automated ML pipelines, FastAPI deployment, Dockerization, CI/CD with GitHub Actions, and cloud deployment on AWS for scalable and reproducible predictions.
Christonikos
"Efficiently deploy, manage, and scale ML models on a cloud platform using FastAPI. Streamline model integration with RESTful APIs, and leverage best practices for containerisation, CI/CD pipelines, and versioning."
Kaustubhshinde11
Designed a scalable ML pipeline with Scikit-learn and MLflow for experiment tracking and version control. • Deployed a production-ready API using FastAPI and Docker for efficient, scalable, and reproducible inference. • Implemented automated data preprocessing and model evaluation to ensure consistent and reliable performance.
A prototype AML (Anti‑Money Laundering) monitoring API built with FastAPI and Pydantic. It ingests transaction messages, applies a cascading classification pipeline (rule‑based → lightweight ML → LLM), and emits risk scores and alerts for suspicious activity. Data validation, model integration, scalable deployment, and real‑time monitoring.
VahantSharma
Production-grade Customer Churn Prediction system built on AWS SageMaker with FastAPI, SHAP explainability, hyperparameter tuning, CI/CD, and Docker. From data validation to deployment — this isn’t just a model, it’s an end-to-end ML engineering pipeline designed for real-world scale.
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