#Ml-ops

Showing 12 of 12 repositories tagged #ml-ops, ranked by stars

PrefectHQ
PrefectHQ
prefect

Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

Score
100
★ 22.9k ⑂ 2.4k +23/day
Python
EthicalML
EthicalML
awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Score
100
★ 20.7k ⑂ 2.6k +19/day
upgundecha
upgundecha
howtheysre

A curated collection of publicly available resources on how technology and tech-savvy organizations around the world practice Site Reliability Engineering (SRE)

Score
0
★ 9.8k ⑂ 887 +4/day
JavaScript
SE-ML
SE-ML
awesome-seml

A curated list of articles that cover the software engineering best practices for building machine learning applications.

Score
0
★ 1.4k ⑂ 125 +2/day
sematic-ai
sematic-ai
sematic

An open-source ML pipeline development platform

Score
50
★ 999 ⑂ 62
Python
suzuran0y
suzuran0y
CCTV-Smartphone-AI-Monitoring

本地监控 + AI 视觉 — LAN-based smartphone-powered AI monitoring framework with structured event output for data acquisition and analysis.

Score
67
★ 634 ⑂ 42 +1/day
Python
viveknaskar
viveknaskar
everything-ai-ml

A curated collection of learning resources for Generative AI, Machine Learning, Agentic AI, LLMs, RAG, Fine-tuning, MLOps, and more.

Score
33
★ 429 ⑂ 56
TypeScript
machine-learning-apps
machine-learning-apps
actions-ml-cicd

A Collection of GitHub Actions That Facilitate MLOps

Score
50
★ 206 ⑂ 41
Jupyter Notebook
Azure-Samples
Azure-Samples
azure-databricks-mlops-mlflow

Azure Databricks MLOps sample for Python based source code using MLflow without using MLflow Project.

Score
0
★ 97 ⑂ 61
Jupyter Notebook
neochaotic
neochaotic
leoflow

GitOps-first, container-native workflow orchestrator in Go (Airflow-UI compatible).

Score
100
★ 39 ⑂ 0 +1/day
Go
NucleusEngineering
NucleusEngineering
hack-your-pipe

Efficient streaming data ingestion, transformation & activation

Score
0
★ 28 ⑂ 3
Python
AmirhosseinHonardoust
AmirhosseinHonardoust
Financial-Fraud-Risk-Engine

A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.

Score
0
★ 20 ⑂ 3
Python
Related Topics
#machine-learning#data-science#python#mlops#data-engineering#observability#ml#data-ops#infrastructure#pipeline#workflow-engine#awesome

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