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Topic

Workshop (4hrs): Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use
Presenters: Nisha Talagala, Sindhu Ghanta

Abstract

Workshop: Bringing Your Machine Learning and Deep Learning Algorithms to Life: From Experiments to Production Use
Presenters: Nisha Talagala, Sindhu Ghanta
Abstract:
In this hands on workshop, attendees will learn how to take Machine Learning and Deep Learning programs into a production use case and manage the full production lifecycle. This workshop is targeted for data scientists, with some basic knowledge of Machine Learning and/or Deep Learning algorithms, who would like to learn how to bring their promising experimental results on ML and DL algorithms into production success. In the first half of the workshop, attendees will learn how to develop an ML algorithm in a Jupyter notebook and transition this algorithm into an automated production scoring environment using Apache Spark. The audience will then learn how to diagnose production scenarios for their application (for example, data and model drift) and optimize their ML performance further using retraining. In the second half of the workshop, users will perform a similar exercise for Deep Learning. They will learn how to experiment with Convolutional Neural Network algorithms in TensorFlow and then deploy their chosen algorithm 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 notebook environments, Spark and TensorFlow engines, as well as an ML lifecycle management environment. For the ML experiments, sample algorithms and public data sets will be provided for Anomaly Detection and Classification. For the DL experiments, sample algorithms and public data sets will be provided for Image Classification and Text Recognition.

Profile

Nisha Talagala is the CEO and founder of Pyxeda AI. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing machine learning in production. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, where she worked on innovation in non-volatile memory technologies and applications. Nisha has more than 20 years of expertise in software development, distributed systems, technical strategy and product leadership. She has worked as technology lead for server flash at Intel - where she led server platform non-volatile memory technology development, storage-memory convergence, and partnerships. Prior to Intel, Nisha was the CTO of Gear6, where she designed and built clustered computing caches for high performance I/O environments.  Nisha earned her PhD at UC Berkeley where she did research on clusters and distributed systems. Nisha holds 63 patents in distributed systems and software, is a frequent speaker at industry and academic events, and is a contributing writer to several online publications.