#ML.
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AI Explainability 360
AI Explainability 360 brings local and global model- and data-explanation methods into a Python toolkit. Its value depends on suitable test data, model access, and disciplined dependency management.
Amazon SageMaker Autopilot
AWS service for automated machine-learning experiments: inspect data, compare model candidates, review reports, and deploy predictions in real time or batches.
Cerebras Wafer-Scale Engine
Cerebras provides wafer-scale AI acceleration for large training and inference workloads where model size, latency and infrastructure fit together.
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.