ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

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cube studio开源云原生一站式机器学习/深度学习/大模型AI平台/MaaS/mlops/人工智能平台/训推平台，算法全链路流程，算力租赁平台，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务，VGPU虚拟化，云边端协同，边缘计算，自动化标注平台，deepseek等大模型sft微调/奖励模型/强化学习训练，vllm/ollama/mindie大模型多机推理，私有知识库llmops智能体，AI模型市场，支持国产异构算力调度,昇腾/寒武纪/海光/摩尔/沐曦等，支持ib/roce/RDMA，支持pytorch/deepspeed/colossalai/ray等分布式

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

Build MLOps Pipelines in Minutes

A turnkey MLOps pipeline demonstrating how to go from raw events to real-time predictions at scale.

Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.

Toy example of an applied ML pipeline for me to experiment with MLOps tools.

MLOps End-to-End Example using Amazon SageMaker Pipeline, AWS CodePipeline and AWS CDK

The MLOps Workload Orchestrator solution helps you streamline and enforce architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services.

A complete end-to-end MLOps pipeline for Marvel character data.

MLOps example using Amazon SageMaker Pipeline and GitHub Actions

The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸

A robust (🐢) and fast (🐇) MLOps tool for managing data and pipelines in Rust (🦀)

This sample demonstrates how to setup an Amazon SageMaker MLOps end-to-end pipeline for Drift detection

Ultimate AWS Data & AI Platform: Real-time flight delay predictions with complete DE, DS, MLOps, Web App & Multi-Agent LLM - All deployed via CDK self-mutating pipelines

Maternal Health Risk prediction MLOps pipeline

Helm chart repository for the new unified way to deploy ClearML on Kubernetes. ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

A local food education app featuring MLOps pipelines.

Amazon SageMaker MLOps deployment pipeline for A/B Testing of machine learning models.

MCP server to connect an MCP client (Cursor, Claude Desktop etc) with your ZenML MLOps and LLMOps pipelines

MLOps pipeline for NVIDIA Merlin on GKE

End-to-end Vehicle Insurance Domain project implementing MLOps. Includes data ingestion, validation, transformation, model training, and deployment with CI/CD pipelines, Docker, and modular project structure. Features Flask app integration, reproducible workflows, and scalable ML lifecycle management.

MLOps with R and Azure Pipelines

This project demonstrates a production-grade MLOps pipeline that deploys a YOLOv11-based face detection service on Google Kubernetes Engine (GKE).

niceML 🍦 is a Python-based MLOps framework designed to streamline the development and maintenance of machine learning projects, offering efficient and scalable pipelines using TensorFlow and Dagster.

Repository with sample code and instructions for creating a complete MLOps training pipeline.