Tags: #mlops

Unsloth Studio provides a unified web interface for efficiently running and training open-source AI models locally across various operating systems and hardware.

An open-source AI engineering platform for managing the complete lifecycle of AI applications, including agents, LLMs, and ML models, from debugging and evaluation to monitoring and optimization.

A Python library for building and deploying high-performance AI model inference APIs and multi-model serving systems with ease.

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.

A command-line tool and VS Code extension for data versioning, ML experiment tracking, and reproducible machine learning pipelines.

An open-source platform for comprehensive AI/ML model observability, evaluation, and debugging.

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

A unified system to run, manage, and scale AI workloads efficiently across any infrastructure, including Kubernetes, Slurm, and over 20 cloud providers, optimizing cost and resource availability.

An open-source Python library for evaluating, testing, and monitoring ML and LLM systems across experiments and production environments.

An open-source, modern-design AI training tracking and visualization tool that integrates with 50+ mainstream frameworks, simplifying experiment management for AI teams.

Kubeflow Pipelines is a Kubernetes-native platform for building, deploying, and managing end-to-end machine learning workflows.

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

ZenML is an open-source AI platform that unifies ML/AI workflows from pipelines to agents, abstracting infrastructure complexity and integrating existing tools.

A collection of Jupyter notebooks and a new Python SDK demonstrating how to build, train, and deploy machine learning models on Amazon SageMaker.

A comprehensive MLOps platform for managing, orchestrating, and scaling the entire machine learning and deep learning lifecycle.

A Python library for building modular, testable, and self-documenting data transformation DAGs with built-in lineage and metadata tracking.

An open-source feature store that streamlines the management and serving of features for AI/ML models, ensuring consistency between training and inference.

ClearML is an open-source MLOps/LLMOps solution that streamlines the entire AI workflow, from experiment management and data versioning to pipeline orchestration and model serving.

An MLOps and LLMOps framework for deploying, managing, and scaling modular, data-centric AI applications and models on Kubernetes.

A comprehensive, curated list of essential MLOps tools and resources, categorized to streamline the machine learning operations workflow.

A comprehensive educational platform for mastering the development, deployment, and iteration of production-grade machine learning applications.

A comprehensive, cloud-native, one-stop platform for machine learning, deep learning, and large language model development, covering the entire MLOps lifecycle.

A free, hands-on course teaching how to build and deploy production-ready LLM and RAG systems, including a personalized 'LLM Twin', using 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.

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 infrastructure platform for data, model, and pipeline orchestration, designed to streamline the development of versatile AI-first applications.