Training & deploying deep learning applications at scale: data management challenges on data science environments

This tutorial provides hands-on coding experience in training, tuning and designing deep learning (DL) data centric applications. It will particularly compare large scale processing environments with respect to underlying data management issues. The tutorial will provide a concise and intuitive overview of the most important aspects of training and application of DL models to label data sets (e.g., images).

The experience of training and deploying DL applications considering data management underlying issues will be done through hands on experiences on top of the data science Databricks solution deployed on a public cloud accessible to students. The tutorial will theoretically and through some practical examples compare different cloud-based data science environments, like AWS, Google Cloud AI, MS Azure AI, and IBM Watson.

Speakers

Genoveva Vargas Solar (http://www.vargas-solar.com) is a French Council of Scientific Research (CNRS) principal researcher. She is a member of the DataBase group of Laboratory on Informatics on Image and Information Systems (LIRIS). She is a regular member of the Mexican Academia of Computing (AMEXCOMP). From 2008 – 2020, duration of the International Research Unit, LAFMIA of the CNRS and the Mexican Government, she was deputy director for Computer Science.

She contributes to the construction of service-based database/data science management systems. The objective is to design data science workflows, new queries, and enactment services guided by Service Level Objectives (SLO). Her work mainly addresses data science queries exploiting graphs. She proposes query evaluation methodologies, algorithms, and tools for composing, deploying, and executing data science functions on just in time architectures (disaggregated data centres). She conducts fundamental and applied research activities for addressing these challenges on different architectures ARM, raspberry, cluster, cloud, and HPC.

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