Moty Fania and Sergei Kom share their experience and lessons learned implementing an AI inference platform to enable internal visual inspection use cases. The platform is based on open source technologies and was designed for real-time, streaming, and online actuation.

Huma Abidi discusses the importance of optimization to deep learning frameworks and shares Xeon performance results and work that Intel is doing with its framework partners, such TensorFlow.

Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper.

Running deep learning (DL) jobs requires end to end workflow to accelerate model training iteratively. It must be scalable on massive data and computational resources, and be framework agnostic to rel …

Deep Learning (DL) is a computational intense form of machine learning that has revolutionize many fields including computer vision, automated speech recognition, natural language processing and artif …

Moty Fania explains how Intel implemented an AI inference platform to enable internal visual inspection use cases and shares lessons learned along the way. The platform is based on open source technologies and was designed for real-time streaming and online actuation.

Amit Kapoor and Bargava Subramanian lead three live demos of deep learning (DL) done in the browserbuilding explorable explanations to aid insight, building model inference applications, and rapid prototyping and training an ML modelusing the emerging client-side JavaScript libraries for DL.

Deep learning model performance relies on underlying data. Dong Meng offers an overview of a converged data platform that serves as a data infrastructure, providing a distributed filesystem, key-value storage and streams, and Kubernetes as orchestration layer to manage containers to train and deploy deep learning models using GPU clusters.