Tags: #mlops

An open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing production-quality AI applications, including agents, LLMs, and ML models.

A Python library for building, deploying, and scaling AI/ML model inference APIs and serving systems.

A human-centric Python framework for building, managing, and deploying real-life AI/ML systems from rapid prototyping to reliable production.

An AI developer platform for tracking, visualizing, and managing machine learning models from experimentation to production.

DVC (Data Version Control) is a command-line tool and VS Code extension for managing data, models, and ML experiments, enabling reproducible machine learning projects.

An open-source platform for debugging, evaluating, and monitoring AI/ML models and pipelines.

A Python framework for building reproducible, maintainable, and modular data engineering and data science pipelines using software engineering best practices.

SwanLab is an open-source, modern-design platform for tracking, visualizing, and analyzing AI/ML training experiments, supporting cloud and self-hosted deployments.

An open-source platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes.

A comprehensive curated list of open-source libraries for deploying, monitoring, versioning, and scaling machine learning models in production.

An open-source AI platform that unifies ML pipelines and agentic workflows, abstracting infrastructure complexity for ML/AI engineers.

A comprehensive MLOps platform for managing, orchestrating, and scaling the machine learning lifecycle with reproducibility and automation.

Apache Hamilton is a lightweight Python library that enables data scientists and engineers to define testable, modular, and self-documenting dataflows (DAGs) with built-in lineage and metadata, portable across any Python environment.

An open-source feature store for AI/ML that streamlines the management and serving of features for model training and online inference.

ClearML streamlines AI/ML/LLM workflows with integrated experiment tracking, data management, MLOps/LLMOps orchestration, and model serving.

An MLOps and LLMOps framework for deploying, managing, and scaling AI systems, from singular models to complex data-centric applications, on Kubernetes.

A comprehensive and categorized collection of awesome MLOps tools and resources, designed to help practitioners navigate the complex MLOps ecosystem.

A comprehensive educational platform teaching developers how to design, develop, deploy, and iterate on production-grade machine learning applications.

A comprehensive practical guide and accompanying code repository for LLM engineers, covering the full lifecycle of building, deploying, and monitoring advanced LLM and RAG applications on AWS with LLMOps best practices.

An open-source, cloud-native, all-in-one MLOps platform designed for the full lifecycle management of machine learning, deep learning, and large language model development and deployment.

A free, hands-on course to build a production-ready LLM & RAG system, including a personalized AI replica, applying LLMOps best practices.

An end-to-end platform for fine-tuning, evaluating, and deploying open-source Large Language Models (LLMs) and Vision Language Models (VLMs).

A vendor-agnostic unified control plane for GPU provisioning and orchestration across clouds, Kubernetes, and on-prem for AI/ML workloads.

A multi-LoRA inference server designed to serve thousands of fine-tuned LLMs on a single GPU, significantly reducing serving costs while maintaining high throughput and low latency.

Distilabel is a framework for generating synthetic data and AI feedback, enabling engineers to build fast, reliable, and scalable AI pipelines based on verified research.

A comprehensive development suite for building, testing, evaluating, deploying, and monitoring high-quality LLM-based AI applications.

An open-source MLOps and AI orchestration platform for building, managing, and automating continuous machine learning and generative AI applications across their entire lifecycle.

An end-to-end AI platform for data, model, and pipeline orchestration, offering ETL, LLM hosting, and RAG capabilities to streamline AI application development.

A user-friendly template integrating PyTorch Lightning and Hydra to streamline deep learning experimentation and development.

A free 9-week online course from DataTalks.Club, designed to teach the fundamentals of MLOps, from experimentation to deployment and monitoring.