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#

ml-monitoring

Here are 22 public repositories matching this topic...

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

  • UpdatedApr 3, 2024
  • Python

Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.

  • UpdatedJun 7, 2023
  • Go
whitebox

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

  • UpdatedJul 11, 2023
  • Python
trubrics-python

Product analytics for AI Assistants

  • UpdatedMay 26, 2025
  • Python

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

  • UpdatedDec 17, 2023
  • Jupyter Notebook

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

  • UpdatedMar 21, 2025
  • Jupyter Notebook

Python SDK for vishwa.ai

  • UpdatedJan 29, 2024
  • Python
anomaly-detection-platform

🕵️🤖 Monitoring a PyTorch Lightning CNN with Weights & Biases

  • UpdatedJul 26, 2021
  • Jupyter Notebook

An agent that exports telemetry for served ML models in TFServing and KFServing.

  • UpdatedMar 27, 2023
  • Go
bodywork-pipeline-with-aporia-monitoring

Integrating Aporia ML model monitoring into a Bodywork serving pipeline.

  • UpdatedJun 20, 2022
  • Jupyter Notebook

This repository give a heads up on how to use machine learning monitoring tool like MLflow to be used as a detached component from the actual ML workflow.

  • UpdatedFeb 2, 2025
  • Python

Production-grade MLOps: Model deployment, monitoring, feature stores, and ML pipelines for real-world AI systems.

  • UpdatedNov 10, 2025
  • Python

End-to-end MLOps pipeline with Airflow ETL orchestration, Redis feature store, and real-time ML monitoring using Prometheus & Grafana with automated data drift detection

  • UpdatedOct 30, 2025
  • Jupyter Notebook

Production-style ML monitoring template on the Wine Quality (red) dataset: Evidently (data/target/prediction drift, data quality) + adversarial validation, PSI/JS effect sizes, SHAP/PDP, slice analysis, and an Alert Policy with actions

  • UpdatedOct 7, 2025
  • Jupyter Notebook

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