Abstract: In this hands on workshop, attendees will learn how to take Deep Learning programs and monitor their health in a production environment. This workshop is targeted for data scientists, with some basic knowledge of Deep Learning algorithms, who would like to learn how to bring their promising experimental results on DL algorithms into production with confidence. Attendees will learn about potential production issues with DL algorithms and how to monitor for these in a production environment using TensorFlow. They will take a sample program in TensorFlow and learn how to deploy it in a production environment. They will learn how to instrument Convolutional Neural Network algorithms in TensorFlow and then deploy their chosen algorithm and instrumentations into production use. They will learn how to monitor the behavior of Deep Learning algorithms in production and approaches to optimizing production DL behavior via retraining and transfer learning.
Attendees should have basic knowledge of ML and DL algorithm types. Deep mathematical knowledge of algorithm internals is not required. All experiments will use Python. Environments will be provided in Azure for hands-on use by all attendees. Each attendee will receive an account for use during the workshop and access to the TensorFlow engines as well as an ML lifecycle management environment. Sample algorithms and public data sets will be provided for Image Classification and Text Recognition.
Bio: Coming Soon
Research Scientist | ParallelM
aiforengineers | west2018training