Awesome MLOps
Section: Model Fairness and Privacy · Library for training machine learning models with privacy for training data.
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Appears in 4 awesome lists
A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
Section: Model Fairness and Privacy · Library for training machine learning models with privacy for training data.
Section: LLM Security & AI Security · Library for training ML models with differential privacy.
Section: Privacy and Safety · A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
Section: Differential Privacy Learning Resources
Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
Curated list of tools and resources related to the use of machine learning for cyber security
Input/output validation framework for building reliable AI applications. Detects and mitigates risks through composable validators for PII, toxicity, prompt injection, and structured output validation. Features Guardrails Hub with 50+ pre-built validators. Apache 2.0 licensed.
NVIDIA's programmable guardrails toolkit: define input, dialog, retrieval, execution, and output rails that intercept the agent loop at five distinct layers using the Colang DSL. The execution rail layer specifically governs what tools the LLM can invoke and what their inputs/outputs may contain —…
🟢🟠 — Open AI gateway with provider routing, fallback and retry controls, guardrail integrations, observability, and MCP traffic support for model and agent applications. (Portkey) — note: the gateway is general infrastructure rather than a standalone security scanner; model-provider…
Python framework for adversarial attacks, data augmentation, and model training in NLP. Augment datasets to increase model robustness and generate adversarial examples. MIT licensed.
is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE) within the main Deep Learning frameworks like…