Labelbox

ML experiment tracking and model monitoring

Weights & Biases is an AI developer platform for experiment tracking, artifact and model lineage, model monitoring, and Weave-based LLM evaluation. It helps teams log runs, compare metrics, manage datasets and model artifacts, and collaborate through dashboards, reports, alerts, SSO/RBAC controls, and hosted or self-managed deployment options.

AI Lakehouse with Feature Store for real-time ML

Hopsworks is a data-intensive AI platform combining a Python-centric Feature Store with MLOps capabilities for production ML systems. Provides sub-millisecond feature retrieval powered by RonDB, dual offline and online storage for batch and real-time inference, experiment tracking, model registry, and deployment pipelines. Available as managed cloud on AWS, Azure, and GCP, self-hosted on Kubernetes, or serverless platform.

Distributed AI compute engine for scaling Python and ML workloads

Ray is an open-source distributed computing framework built for scaling AI and Python applications from a laptop to thousands of GPUs. It provides libraries for distributed training, hyperparameter tuning, model serving, reinforcement learning, and data processing under a single unified API. Ray's public site highlights OpenAI and other enterprise users. Maintained by Anyscale with Apache-2.0 open-source licensing.

Unified framework for fine-tuning 100+ large language models

LLaMA-Factory is an open-source toolkit providing a unified interface for fine-tuning over 100 LLMs and vision-language models. It supports SFT, RLHF with PPO and DPO, LoRA and QLoRA for memory-efficient training, and continuous pre-training. The LLaMA Board web UI enables no-code configuration, while CLI and YAML workflows serve advanced users. Integrates with Hugging Face, ModelScope, vLLM, and SGLang for model deployment.

2x faster LLM fine-tuning with 70% less VRAM on a single GPU

Unsloth is an open-source framework for fine-tuning large language models up to 2x faster while using 70% less VRAM. Built with custom Triton kernels, it supports 500+ model architectures including Llama 4, Qwen 3, and DeepSeek on consumer NVIDIA GPUs. Unsloth Studio adds a no-code web UI for dataset creation, training observability, model comparison, and GGUF export for Ollama and vLLM deployment.

Microsoft's open-source frontier voice AI for long-form multi-speaker audio

VibeVoice is Microsoft's open-source voice AI family with both TTS and speech recognition models. The TTS model generates up to 90 minutes of expressive multi-speaker audio with 4 distinct voices. VibeVoice-ASR transcribes 60-minute recordings in a single pass with speaker identification and timestamps. Built on continuous speech tokenizers at 7.5 Hz and next-token diffusion, it compresses audio 80x more efficiently than Encodec while preserving fidelity.

High-performance OLTP graph-vector database in Rust built on object storage for AI memory

HelixDB is an open-source, unified graph-vector database engineered in Rust that merges relational, graph, and vector workloads into a single OLTP engine, using LMDB local caching and S3 object storage for scalable agent memory.

Modular graph-based RAG pipeline using hierarchical knowledge graph community summaries

Microsoft GraphRAG is an open-source retrieval framework that transforms unstructured text into structured knowledge graphs, clusters entities hierarchically using the Leiden algorithm, and generates dataset-wide summaries alongside entity-level local search for multi-hop reasoning.