Join PyData Montreal for their virtual meetup, proudly sponsored by the IBM Data Science Community and featuring the following two talks:

Text Extensions for Pandas (Frederick Reiss, IBM)
Most areas of Python data science have standardized Pandas DataFrames for representing and manipulating structured data in memory. Natural Language Processing, not so much. In this presentation, we'll explain why you should be using Pandas for NLP. DataFrames make every phase of NLP easier, from creating new models, to evaluating their effectiveness, to building applications that integrate those models. We'll talk about our open source library, Text Extensions for Pandas (https://ibm.biz/text-extensions-for-pandas), which adds special data types and library integrations specifically geared to NLP use cases. We'll explain how these extensions connect to some basic NLP concepts, and then we'll finish with an example of using Pandas to build an NLP application.

Introducing Elyra: Extending JupyterLab for AI (Luciano Resende, IBM)
Creating an AI pipeline often involves learning/writing another layer of code to orchestrate the flow of information. Managing environments, artifact handling and system resources can feel daunting for those unfamiliar with the infrastructure side of AI. Elyra's pipeline editor abstracts patterns in workflow development to provide a friendly and familiar interface in JupyterLab in a NoCode/LowCode fashion and integrates with workflow orchestrators like Kubeflow Pipelines and Apache Airflow. This presentation will detail how Elyra, an Open Source project, creates AI pipelines and executes them locally or in external runtimes such as Kubeflow Pipelines and Apache Airflow, all without having to leave your JupyterLab development environment. We will also look at other useful Elyra functionality that helps data scientists overcome the day-to-day model development complexities, all these using live demos throughout the presentation.




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