Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
An open-source ML pipeline development platform
本地监控 + AI 视觉 — LAN-based smartphone-powered AI monitoring framework with structured event output for data acquisition and analysis.
Open-source data management for multimodal AI. Query, trace, and govern with a lineage-native, format-agnostic lakehouse for agents and teams. Supports biological formats and registries - by the creators of Scanpy. 🍊YC S22
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
Find the samples, in the test data, on which your (generative) model makes mistakes.
Efficient streaming data ingestion, transformation & activation
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.
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.
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Kling AI Python SDK - Production-ready, type-safe Python client for Kling AI's cutting-edge video generation and media processing APIs. Supports async/await, Pydantic models, and comprehensive error h
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
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.
Config-driven PyTorch research template: Hydra configs + plugin registry for classification experiments.
A prefect extension that builds on top of the task decorator to reduce negative engineering!
Repo for running Whylogs as part of a CI workflow using github actions.
Observability for RAG and ML pipelines. Traces every stage, shows what was retrieved, what got dropped and why. Python SDK + dashboard
Stop silent data leakage in ML training pipelines.
Self-hosted scheduler with a web UI for the scripts, notebooks and SQL you already have: schedules, retries, backfills, approval gates and a full run history. One process, one SQLite file.
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