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This reference implementation provides an AI-enabled approach to prediction and detection of bearing failure by analyzing vibration data.

Introduces RayOnSpark, a distributed open source framework from UC Berkeley RISELab that supports big data applications such as reinforcement learning

Real-time Sensor Fusion for Loss Detection allows you to deploy sensor fusion technology for loss detection at self-checkout.

Intelligent Traffic Management is designed to detect and track vehicles and pedestrians and estimate a safety metric.

Driver Management

Driver Management Reference Implementation is designed to detect and track driver behavior and actions to help prevent dangerous situations.

Use TensorFlow* performance Jupyter* notebooks to analyze the performance benefit of Intel® Optimizations for TensorFlow*.

This is a ResNet50 v1.5 FP32 inference model package optimized with TensorFlow* with artifacts needed to run on bare metal.

This is a ResNet50 v1.5 FP32 inference container optimized with TensorFlow*.

These containers include Intel® Distribution for Python* Full and default to starting with a bash shell.

These containers include Intel® Distribution for Python* Core and default to starting with a bash shell.

OpenVINO™ DL Workbench is a web-based environment to visualize, fine-tune, and compare performance of deep learning models on Intel® architectures.

This is a GNMT FP32 inference container optimized with TensorFlow*.