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Running Massively Parallel Deep-Learning Inference Pipelines On Kubernetes Martin Abeleda & Suneeta Mall, Nearmap

Nearmap captures terabytes of aerial imagery daily. With the introduction of artificial intelligence (AI) capabilities, Nearmap has leveraged Kubernetes to generate AI content based on tens of petabytes of images effectively and efficiently. Martin Abeleda and Suneeta Mall from Nearmap discuss how using Kubernetes as the backbone of their AI infrastructure, allowed them build a fully automated deep-learning inferential pipeline that despite not being embarrassingly parallel is actually massively parallel. This talk also explains the architecture of this auto-scalable solution that has exhausted all K80 spot GPUs across all US data centres of AWS for weeks. 


GE Aviation Spark Application – Experience Porting Analytics into PySpark ML Pipelines

GE is a renowned world leader manufacturer of commercial jet engines, offering products for many of the best-selling commercial airframes. With more than 33,000 engines in service, GE Aviation has a history of developing analytics for monitoring its commercial engines fleets. In this talk you will learn how analytic tools such as SQL Server and MATLAB were used until recently, when GE’s data was moved to an Apache Spark environment. Consequently, GE Aviation's advanced analytics are now being migrated to Spark, where there should also be performance gains with bigger data sets. Dr Peter Knight a senior data scientist with the GE Aviation UK data science team and Honor Powrie a Director of Data and Analytics at GE Aviation share their experiences of converting advanced algorithms to custom Spark ML pipelines, as well as outlining various case studies.


Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Love to Scale

Luca Canali an engineer and team lead at the CERN Hadoop, Spark and database services at CERN shares the experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. He covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services.