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#

ml-ops

Here are 68 public repositories matching this topic...

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

  • UpdatedDec 18, 2025
  • Python
howtheysre

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

  • UpdatedNov 17, 2025
  • JavaScript

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

  • UpdatedMar 26, 2024
sematic

An open-source ML pipeline development platform

  • UpdatedJan 9, 2025
  • Python
flama

A data framework for biology. Makes your data queryable, traceable, reproducible, and FAIR. One API: lakehouse, lineage, feature store, ontologies, LIMS, ELN.

  • UpdatedDec 17, 2025
  • Python

A Collection of GitHub Actions That Facilitate MLOps

  • UpdatedNov 21, 2022
  • Jupyter Notebook

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

  • UpdatedMar 21, 2025
  • Jupyter Notebook

The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt

  • UpdatedDec 15, 2025
  • Python

Find the samples, in the test data, on which your (generative) model makes mistakes.

  • UpdatedOct 16, 2024
  • Python

Efficient streaming data ingestion, transformation & activation

  • UpdatedMay 1, 2023
  • Python

Curated examples and patterns for using Chalk. Use these to build your feature pipelines.

  • UpdatedDec 10, 2025
  • Python

Designing IT and ML Applications using Systems Thinking Approach at IIT Bhilai (CS559)

  • UpdatedMay 5, 2024

Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]

  • UpdatedJul 20, 2021
  • Python

A complete machine-learning system that predicts AI assistant user satisfaction using behavioral signals such as device, usage category, time features, session metrics, and model metadata. Includes full ML pipeline, SHAP explainability, evaluation suite, and an interactive Streamlit analytics dashboard.

  • UpdatedDec 5, 2025
  • Python

Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring

  • UpdatedNov 12, 2022
  • Python

Vehicle data classification (supervised, unsupervised learning)

  • UpdatedMay 23, 2023
  • Jupyter Notebook

This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.

  • UpdatedJul 14, 2025
  • Python

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