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Thursday, May 9 • 1:00pm - 1:45pm
Architecting to Support Machine Learning

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Machine learning (ML) is everywhere, and there is a lot of material that discusses many of the difficulties associated with creating an ML solution from a data science point of view. This work covers aspects related to obtaining data, selecting a particular algorithm, and training and testing the algorithms. There is, however, less information related to architecting the software system where this algorithm will be running once it is in production. In this talk, we want to address this topic, both from a theoretical and practical point of view. Our goal is to help software architects that need to design systems that support ML by identifying common architecture design considerations for various phases of data processing, training, and model serving.

We will cover the following topics:

1. Where does software architecture fit in systems that support ML?

2. A framework for gathering primary architectural decisions of systems that support ML. These decisions include aspects such as
* type of training of the model and model location
* time of training: offline vs. online
* time of prediction: batch vs. on demand
* location of prediction: cloud vs. device
* technological choices
* other considerations

3. Several case studies of systems developed at SoftServe that support ML using the previously discussed framework.

4. Synthesis: What needs to be considered when architecting ML systems?
* Lessons learned
* Design process considerations

See the slides.

Watch the video.

avatar for Humberto Cervantes

Humberto Cervantes

SATURN 2019 Technical Co-Chair, Universidad Autónoma Metropolitana Iztapalapa
Humberto Cervantes is a professor at Universidad Autónoma Metropolitana Iztapalapa in Mexico City. His primary research interest is software architecture and, more specifically, the development of methods and tools to aid in the design process. He is active in promoting the adoption... Read More →
avatar for Rick Kazman

Rick Kazman

Professor / Research Scientist, University of Hawaii / Software Engineering Institute
Rick Kazman is a professor at the University of Hawaii and a research scientist at the Carnegie Mellon University Software Engineering Institute. Kazman has created several influential methods and tools for architecture analysis, including the Software Architecture Analysis Method... Read More →
avatar for Iurii Milovanov

Iurii Milovanov

SoftServe, Inc.
Iurii Milovanov is a Data Science Practice Leader for SoftServe with more than 8 years of industry experience in building enterprise-level AI and Big Data solutions. He is a computer science expert with strong emphasis on cutting-edge technologies. His research interests include various... Read More →

Thursday May 9, 2019 1:00pm - 1:45pm EDT
Grand Station 4 Sheraton Pittsburgh Hotel at Station Square