Simple, secure, and reproducible packaging for AI/ML projects.

KitOps is an open source DevOps tool that packages and versions your AI/ML model, datasets, code, and configuration into a reproducible artifact called a ModelKit. ModelKits are built on existing standards, ensuring compatibility with the tools your data scientists and developers already use.

• KitOps @ GitHub.

MLOps Attack Toolkit

MLOKit is a toolkit that can be used to attack MLOps platforms by taking advantage of the available REST API. This tool allows the user to specify an attack module, along with specifying valid credentials (API key or stolen access token) for the respective MLOps platform. The attack modules supported include reconnaissance, data extraction and model extraction. MLOKit was built in a modular approach, so that new modules can be added in the future by the information security community.

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

Amazon SageMaker is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case. With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE).

• Déployez vos modèles de Machine Learning avec Amazon SageMaker @ Cockpit io :fr:.

Contribute to krishnaik06/The-Grand-Complete-Data-Science-Materials development by creating an account on GitHub.