Shapash
MAIF
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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Results: 29
MAIF
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
TanStack
🤖 Type-safe, provider-agnostic TypeScript AI SDK for streaming chat, tool calling, agents, and multimodal apps across OpenAI, Anthropic, Gemini, React, Vue, Svelte, and…
Trusted-AI
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
huggingface
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
fairlearn
A Python package to assess and improve fairness of machine learning models.
A library for training and deploying machine learning models on Amazon SageMaker
modelscope
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
bentoml
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
ThinkInAIXYZ
🐬DeepChat - A smart assistant that connects powerful AI to your personal world
BlockRunAI
The agent-native LLM router for OpenClaw. 41+ models, <1ms routing, USDC payments on Base & Solana via x402.
alvinreal
Curated list of the best truly open-source AI projects, models, tools, and infrastructure.
huggingface
A blazing fast inference solution for text embeddings models
thunderbird
AI You Control: Choose your models. Own your data. Eliminate vendor lock-in.