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A comprehensive repository has been created to showcase open-source libraries that facilitate the deployment, monitoring, and scaling of machine learning models. This collection includes tools for various aspects of machine learning operations, such as data labeling, model tracking, and adversarial robustness. The repository aims to support developers and researchers in efficiently managing their machine learning workflows.
The repository features a wide range of categories, including AutoML, data pipelines, and evaluation tools, making it a valuable resource for machine learning practitioners. Users can stay updated on new additions by following the repository and can also access a search toolkit for easy navigation. This initiative reflects the growing importance of ethical and efficient machine learning practices in the industry.
• The repository offers a curated list of machine learning libraries.
• It includes tools for deployment, monitoring, and scaling ML models.
MLOps refers to the practices that aim to automate and improve the deployment and management of machine learning models.
AutoML encompasses techniques that automate the process of applying machine learning to real-world problems, making it accessible to non-experts.
Adversarial robustness involves developing models that can withstand adversarial attacks, ensuring their reliability in real-world applications.
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