✨ Build a machine learning model from a prompt
-
Updated
Mar 6, 2026 - Python
GeezerVisonThe World Wide Web, restored to 800 pixels of glory. |
| Home|About|Gaming cabinet|Archives|How it works|Starter sites |
GeezerVison is showing github.com — Build software better, together
#
mlengineeringHere are 17 public repositories matching this topic...Language: All
Filter by language
Sort: Most stars
Sort options
✨ Build a machine learning model from a prompt
Cryptocurrency trading engineering: A scalable back testing infrastructure and a reliable, large-scale trading data pipeline
ML Engineering best practices | Deploying an ML API with FastAPI, PostgreSQL, and Docker for scalable model storage and inference.
ML Engineering best practices | ML model deployment as a FastAPI service, containerized with Docker for scalability and reproducibility.
End-to-end MLOps project demonstrating production ML engineering: automated data pipelines, multi-model training, MLflow tracking, model registry, FastAPI deployment, drift detection, DVC, Docker, and GitHub Actions CI/CD.
Enterprise reference architecture for Agentic AI systems using LangGraph, RAG, knowledge graphs, and LLM reasoning to power autonomous operational decision workflows.
opensource rag knowledgegraphs mlengineering genai enterpriseai langgraph agenticai aiarchitecture airlinetech
This repository contains my implementations, experiments, and projects related to Machine Learning algorithms, models, and data science workflows.
Resume of Jack J. Burleson, 2026 ⚚ M.S. candidate, Data Science in Human Behavior, UW–Madison
Computer vision pipeline for pasture biomass prediction using DINOv3 embeddings, Ridge regression, reproducible inference, Pytest, and GitHub Actions.
machine-learning computer-vision scikit-learn ml pytorch pytest transfer-learning github-actions mlengineering dinov3
DriftWatch is an intelligent ML drift detection and response system designed to help teams detect data shifts, understand their potential impact, and make cost-aware decisions about monitoring and intervention
research ai ml drift model-evaluation mlops drift-detection drift-monitoring drift-diffusion-model mlengineering drift-detection-mlops
Repository for project work of Udacity's Azure ML Engineer nanodegree.
Hand-implementing an N-BEATS model from the paper, to compare it against the classical methods (SARIMA and ETS) on large mortgage-related datasets
It is easy to prototype ML models. With higher levels of abstraction, the expertise required to build any ML model is decreasing each day.
🔐 Production-grade credit card fraud detection system — XGBoost + SMOTE on 284k transactions (99.9% acc · 93.2% F1 · 98.7% AUC). FastAPI serving, Kubernetes deployment with HPA autoscaling, full MLOps stack: DVC · MLflow · Prefect · GitHub Actions CI/CD · 90%+ test coverage.
data-science machine-learning docker-compose ml python3 xgboost fraud-detection imbalanced-learning prefect mlflow fastapi smote-sampling gitactions-workflow mlops-workflow mlengineering
Lets atomic distro users use NvidiaSync via toolbox
Add this topic to your repoTo associate your repository with the mlengineering topic, visit your repo's landing page and select "manage topics." |
| You are visitor 01402503 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required. |