Introduction to Elyra: Set of AI-centric extensions to JupyterLab

Abstract: Whether you are just getting started in Data Science or are seasoned data scientist, the JupyterLab IDE is likely a tool you are using frequently to get work done. In this session we will introduce Elyra - a set of AI-centric extensions to JupyterLab - that provide support for ML workflow pipelines, Git versioning, code snippets and much more. We'll also demonstrate how to create Machine Learning pipeline from Jupyter notebooks or Python scripts using Elyra's Visual Pipeline Editor, and how to run pipelines locally in JupyterLab or remotely on Kubeflow Pipelines.

Short Bio: Upkar Lidder is senior software engineer with 10+ years in IT development including team management, functional and technical leadership roles with a deep experience in full stack technology. Currently focused on Machine Learning and Artificial Intelligence. He can be seen speaking at various conferences and participating in local tech groups and meetups. Upkar went to graduate school in Canada and currently resides in the United States.

Patrick Titzler is a developer advocate with the Center for Open Source Data and AI Technologies at IBM. For the past couple of years, he has contributed to several Data and AI open source projects, such as Elyra (a set of AI centric extensions to JupyterLab), the Data Asset Exchange (a curated collection of open data sets for the enterprise), and the Model Asset Exchange (a curated collection of open source deep-learning models). In his previous role as an advocate for IBM's cloud data platform, Patrick focused on open source based data services, such as Apache CouchDB and Apache TinkerPop.

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