An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

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.

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.

🕹️ Performance Comparison of MLOps Engines, Frameworks, and Languages on Mainstream AI Models.

ForML - A development framework and MLOps platform for the lifecycle management of data science projects

FuseML aims to provide an MLOps framework as the medium dynamically integrating together the AI/ML tools of your choice. It's an extensible tool built through collaboration, where Data Engineers and DevOps Engineers can come together and contribute with reusable integration code.

A curated list of awesome tools, frameworks, platforms, and resources for building scalable and efficient AI infrastructure, including distributed training, model serving, MLOps, and deployment.

This is the code repository for the AI project template. The idea of this template is to have a code framework prepared for any AI/ML/MLOps/LLMOps project

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.

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Complete MLOps framework for Vertex-AI

A collection of portable workflows, automation recipes and components for MLOps in a unified CK format. Note that this repository is outdated - please check the 2nd generation of the CK workflow automation meta-framework with portable MLOps and DevOps components here:

An AI-powered MLOps engine for self-evolving remote sensing object detection, driven by a Multi-Agent system based on the IDEATE framework.

Anacostia is a framework for creating machine learning operations (MLOps) pipelines

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multi-agents framework of LLM for MLOps

Comprehensive MLOps project covering the end-to-end machine learning lifecycle. Includes tools, frameworks, and best practices for model development, deployment, monitoring, and automation.

Aigear is a flexible MLOps framework that supports cloud deployment. Developers can customize the framework based on their own knowledge to reduce its difficulty.

Kubernetes-based, open-source MLOps framework

LLM orchestration, AI-native framework, Tree-of-Thought, agentic workflows, dual-scientist debate, reproducible R&D, SBOM, provenance, continuous evaluation, risk tiers, Spark Structured Streaming, Delta Lake, MLOps, evaluation gates.

Time Series Forecasting Method in Finance and Stocks industry use many frameworks serving MLOPs application.

Official implementation of Retraining-Efficiency Score (RES). A Green AI framework to reduce retraining costs by 50% in MLOps pipelines.

A curated list of tools, frameworks, platforms, and resources for Machine Learning Operations (MLOps).

A framework for conducting MLOps.

A complete production-ready MLOps framework with built-in distributed training, monitoring, and CI/CD. Deploy ML models to production with confidence using our battle-tested infrastructure.

🔥 Awesome AI Tools & Frameworks of 2025: LLMs, APIs, MLOps, Prompts, Datasets

This repository contains a collection of papers related to Machine Learning Operations (MLOps), which is the practice of applying DevOps principles and techniques to machine learning projects.The papers are organized into different categories, such as MLOps Overview, MLOps Frameworks, MLOps Techniques, and MLOps Applications.

A simple project intended to be a test ground for various MLOps frameworks, scripts, bundles, etc.

Open-source PyTorch-Geometric framework for GNN analysis, interpretation & defense. Features 8 explanation methods, 7 attacks (Nettack, PGD), 7 defenses (GNNGuard), no-code GUI, graph visualizations & MLOps support.

A coleção definitiva e curada de recursos de Inteligência Artificial (AI), Machine Learning (ML) e Deep Learning (DL). Inclui frameworks, modelos, datasets, MLOps, LLMs e aplicações financeiras/comerciais.