#ML.
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AI Explainability 360
AI Explainability 360 is an open-source toolkit designed to improve the explainability of machine learning models. It offers a range of algorithms and methods to make predictions and decisions of AI systems more understandable and transparent. The toolkit supports various models and applications, from simple classifiers to complex neural networks.
Cerebras Wafer-Scale Engine
The Cerebras Wafer-Scale Engine (WSE) is a revolutionary AI accelerator designed for complex machine learning and deep learning applications. Unlike traditional processors, the WSE uses a unique architecture that utilizes an entire wafer as a single unit, enabling enormous computational power and bandwidth optimized for complex AI models and large datasets.
Intel Habana Labs
Intel Gaudi accelerators and software stack for AI training and inference in professional infrastructure.
InterpretML
Open-source package for interpretable machine learning, explanations, and model diagnostics.
Microsoft Azure Automated ML
Microsoft Azure Automated ML is a cloud-based service for automatically creating, training, and deploying machine learning models. It helps data science teams, developers, and analysts build predictive models efficiently with automated algorithm selection, feature engineering, and scalable Azure integration.