Hugging Face and Intel – Driving Towards Practical, Faster, Democratized and Ethical AI solutions‪.‬ Code Together

Transformer models are the powerful neural networks that have become the standard for delivering advanced performance behind these innovations. But there is a challenge: Training these deep learning models at scale and doing inference on them requires a large amount of computing power. This can make the process time-consuming, complex, and costly.
Today we will talk about all kinds of issues around accessible, production level AI solutions. We also talk about ethical questions around AI usage and why open, democratized AI solutions are important.
Learn more:
Hugging Facehttps://huggingface.co
Hugging Face Hubhttps://huggingface.co/models
Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Acceleratorhttps://huggingface.co/blog/habana-gaudi-2-bloom
Accelerating Stable Diffusion Inference on Intel CPUshttps://huggingface.co/blog/stable-diffusion-inference-intel
Transformer Performance with Intel & Hugging Face Webinarhttps://www.intel.com/content/www/us/en/developer/videos/optimize-end-to-end-transformer-model-performance.html#gs.pomt5k
Intel Explainable AI Toolshttps://github.com/IntelAI/intel-xai-tools
Intel Distribution of OpenVINO Toolkithttps://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html
Intel AI Analytics Toolkit (AI Kit)https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html
Guests:
Julien Simon – Chief Evangelist @ Hugging Face
Ke Ding – Principal Engineer @ Intel