An open-source AI monitoring and governance engine providing LLM hallucination detection, PII identification, prompt injection defense, and traditional ML model evaluation, featuring real-time guardrails and OpenInference support.
An open-source, all-in-one platform combining web analytics, session replay, feature flags, and A/B testing for product teams to build successful products.
An interactive open-access textbook on Machine Learning Systems engineering from Harvard University, integrating the TinyTorch framework with hands-on edge deployment labs, covering the full spectrum from ML fundamentals to system optimization.
An AI-powered data science team of agents that automates data loading, cleaning, feature engineering, EDA, visualization, and machine learning modeling (H2O + MLflow) through specialized agent collaboration, featuring a Streamlit visual pipeline studio to perform common data science tasks 10X faster.
An AI-driven multi-agent research assistant based on LangGraph that automates the entire research workflow from hypothesis generation, data analysis, and visualization to comprehensive report writing.
The official inference framework for 1-bit Large Language Models by Microsoft. It features optimized kernels for lossless, high-speed inference on CPUs and GPUs, drastically reducing energy consumption and enabling 100B+ parameter models to run on local consumer hardware.
A curated collection of recent research papers on autonomous agents, focusing on both reinforcement learning-based and large language model-based approaches, helping researchers quickly understand the cutting edge of the field。
A Python tutorial repository providing runnable examples and theoretical explanations for deep reinforcement learning, classical reinforcement learning, and machine learning concepts.
A Python library for orchestrating zero-shot computer vision models, enabling custom end-to-end pipeline creation without needing to collect and annotate large training datasets.
A comprehensive practical project for building Retrieval-Augmented Generation (RAG) systems, covering the full implementation process from basic to advanced techniques, including system design, evaluation, and optimization.
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