Abstract: 

Bidirectional Encoder Representations from Transformers (BERT) is currently one of the most widely used NLP models. The combination of OpenDataHub, Intel® oneAPI AI Analytics Toolkit (AI Kit), and OpenVINO Toolkit helps operationalize models like BERT following MLOps best practices. As a starting point, OpenDataHub provides a notebook as a service environment through it's JupyterHub implementation. We will show how data scientists, using custom resources, can initiate training of BERT models using AI Kit images with Intel optimized deep learning frameworks like PyTorch and Tensorflow. OpenVINO integrations with OpenDataHub augment its image catalog to include pre-validated notebook images that can be used to optimize or optionally fine-tune for lower precision models like BERT. Finally, we detail how to operationalize optimized and scalable inference on a multi-node Xeon CPU cluster using OpenVINO model server and Istio service mesh.

Bio: 

Kyle is the Data Foundation Architect covering both OpenShift Data Foundation and Red Hat Ceph Storage products at Red Hat. His focus is at the intersection of open source, distributed storage systems, data engineering, and machine learning.