Red Hat OpenShift / ODSC West 2021 | Open Data Science Conference

This keynote will dive deep into the real-world success stories, including lessons learned from various industries on how these organizations built and deployed AI projects on Kubernetes powered hybrid cloud platform. It will also share findings from an industry survey of Technology Executives around MLOps deployment trends for Kubernetes and Hybrid Cloud.

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 it's 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.

The adoption of Machine Learning in conjunction with traditional Decision Management has increased over the last few years: user data can be easily collected and processed so it is crucial now to leverage similar information to build Intelligent Applications where Machine Learning and Decision Management combine.
Similar integrations can also be achieved by using many different technologies, proprietary or based on open standards. Red Hat embraces open source and open standards and the Red Hat Decision Manager platform offers the possibility to use DMN (Decision Model and Notation) and PMML (Predictive Modeling Markup Language) standards to represent decisions and predictive models well integrated together.
During this session, attendees will have the opportunity to learn more about the Trustworthy Decision Management concept and see how it can be applied to Decision Managed and Hyperautomation solutions that run natively in the cloud.

In this talk, we will present how we are implementing the OS-Climate data commons platform based on a data mesh architecture to make data accessible, available, discoverable, and interoperable across various development streams, while supporting strict compliance requirements around data access and regulatory disclosures. We will explain how the OS-Climate platform leverages open source tools including KubeFlow, Trino, Jupyter, Elyra pipelines and OpenShift to build maintainable and collaborative pipelines, and federate heterogeneous data sources into a controlled, common data resource for our community of climate data scientists and financial sector stakeholders.

In this talk, we will present how we are implementing the OS-Climate data commons platform based on a data mesh architecture to make data accessible, available, discoverable, and interoperable across various development streams while supporting strict compliance requirements around data access and regulatory disclosures. We will explain how the OS-Climate platform leverages open source tools including KubeFlow, Trino, Jupyter, Elyra pipelines and OpenShift to build maintainable and collaborative pipelines, and federate heterogeneous data sources into a controlled, common data resource for our community of climate data scientists and financial sector stakeholders.