Tag

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search. It powers AI applications by efficiently organizing and searching vast amounts of unstructured data, such as text, images, and multi-modal information.

ONNX (Open Neural Network Exchange) is an open ecosystem that provides an open source format for AI models, including deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types, focusing on inferencing capabilities. Widely supported across frameworks and hardware, it enables interoperability and accelerates AI innovation.

Streamlit is an open-source app framework that turns Python scripts into shareable web apps in minutes. It enables data scientists and AI/ML engineers to build interactive data apps like dashboards, reports, or chat apps using pure Python, without front-end experience.

NautilusTrader is an open-source, high-performance event-driven algorithmic trading platform and backtester by Nautech Systems. Its Rust-based core with Python bindings provides parity between research/backtest and production/live deployments, supports multi-venue and multi-asset strategies, advanced order types, optional high-precision numeric modes, and is fast enough to be used to train AI trading agents (RL/ES).

CleanRL is a high-quality, single-file implementation library for deep reinforcement learning (Deep RL). It provides compact, research-friendly standalone implementations of many RL algorithms (PPO, DQN, C51, DDPG, TD3, SAC, PPG, etc.), benchmarks, TensorBoard logging, Weights & Biases integration, and cloud/run tooling. It emphasizes readability, reproducibility, and ease of understanding rather than being a modular importable framework.

YOLOv5 is an open-source PyTorch-based computer vision repository by Ultralytics, focused on real-time object detection and extended support for segmentation and classification. It is known for ease of use, speed, multiple pre-trained model sizes, and broad export/deployment support (ONNX, TFLite, CoreML, TensorRT). The repo includes training, inference scripts, tutorials, and integrations for production-ready workflows.

Qlib is an open-source, AI-oriented quantitative investment platform from Microsoft that provides a full pipeline for quant research — data processing, feature engineering, model training, backtesting and serving. It supports supervised learning, market-dynamics modeling and reinforcement learning, and integrates tools (e.g., RD-Agent) for automated factor mining and model optimization.

SkyPilot is an open-source MLOps / AI infrastructure project that provides a unified control plane and CLI to run, manage, and scale AI workloads on any compute — Kubernetes, Slurm, 20+ clouds, or on-prem clusters. It supports job-as-code (YAML/Python), intelligent scheduling and cost optimization (spot instances, autostop), automatic setup/sync of environments, auto-recovery, and integrations for training, serving and inference workflows.

ExecuTorch is PyTorch’s unified on-device AI deployment solution for mobile, embedded, and edge devices. It enables direct export from PyTorch, ahead-of-time compilation, quantization and hardware partitioning to produce compact runtime programs (.pte) that run across many backends (XNNPACK, Vulkan, CoreML, Qualcomm, etc.). It supports LLMs, vision, speech and multimodal models with a small runtime footprint and production tools for profiling, memory planning, and selective operator builds.

Ultralytics' open-source YOLO framework and toolkit providing end-to-end workflows for training, evaluation and deployment. Supports object detection, instance segmentation, pose estimation, classification and tracking. Built on PyTorch with a user-friendly CLI and Python API for research and production.

Pathway is a Python ETL and live data framework combining a user-friendly Python API with a high-performance Rust engine. It supports both batch and streaming pipelines, stateful transformations, persistence, numerous connectors, and includes LLM/RAG tooling for real-time analytics and live LLM pipelines.

Ragas is an open-source toolkit by VibrantLabs for evaluating and optimizing large language model (LLM) applications. It offers objective metrics (LLM-based and traditional), automated test-data generation, integrations with popular LLM frameworks and observability tools, and utilities for building feedback loops to improve production LLMs.