Standardizing Deep Learning Model Deployment

Got this more detailed invite late,  and the talk is over,  but below you can follow along on slides and talk, see below:

Nick Pentreath, IBM : "Standardizing deep learning model deployment with the Model Asset Exchange"   Technical talk

Background :

Nick Pentreath is a principal engineer in IBM's Center for Open-source Data & AI Technology (CODAIT), where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations. He has also worked at Goldman Sachs, Cognitive Match, and Mxit. He is a committer and PMC member of the Apache Spark project and author of "Machine Learning with Spark". Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.

Task Description : The popular version of applying deep learning is that you take an open-source or research model and simply deploy it. However, in reality developers and data scientists face many challenges ranging from custom requirements for data pre- and post-processing, to inconsistencies across frameworks, to lack of standardization in serving APIs. The goal of the IBM Developer Model Asset eXchange (MAX) is to remove these barriers to entry for developers to obtain, train and deploy open-source deep learning models for their enterprise applications. For model deployment, MAX provides container-based, fully self-contained model artefacts, encompassing the end-to-end deep learning predictive pipeline and exposing a standardized REST API. This talk explores the MAX deployment and serving framework, covering best practices for cross-framework, standardized deep learning model deployment  .... 

Slides and recording of the talk now posted:   http://cognitive-science.info/community/weekly-update/ 

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