Abstract: Putting machine learning models into production is now mission critical for every business - no matter what size.
TensorFlow is the industry-leading platform for developing, modeling, and serving deep learning solutions. But putting together a complete pipeline for deploying and maintaining a production application of AI and deep learning is much more than training a model. Google has taken years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of tools and libraries that Google uses internally.
Learn what’s involved in creating a production pipeline, and walk through working code in an example pipeline with experts from Google. You’ll be able to take what you learn and get started on creating your own pipelines for your applications.
Bio: Jarek is the Technical Program Manager at Google Research responsible for TensorFlow Extended (tensorflow.org/tfx). TFX is the Google-production-scale end-to-end machine learning platform based on TensorFlow. Jarek joined Google in 2010, has a MS in Software Management from Carnegie Mellon University and BS Computer Science with concentration on AI from The University of Memphis. Prior to joining Google, he worked on building telecommunications systems software at Hewlett Packard, BEA Systems, Alcatel and a couple of startups