All Software Architecture, All the Time
June 10-13, 2019
San Jose, CA

Artificial Intelligence sessions

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1:30pm–5:00pm Tuesday, June 11, 2019
Managed machine learning systems and internet of things
Location: 230 B
Secondary topics:  Framework-focused
Noah Gift (UC Davis ), Robert Jordan (Pragmatic AI Labs)
Average rating: **...
(2.25, 4 ratings)
The next evolution of AI and ML is cloud native, managed platforms, and custom-hardware AI. Noah Gift and Robert Jordan teach you how to use managed AI and ML platforms to create solutions in a fraction of the time as a “roll your own" ML solution. Join in to see how these cloud-managed solution compare so you can pick the right solution for the task at hand. Read more.
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9:00am–10:30am Thursday, June 13, 2019
Architecting and deploying machine learning for private cloud
Location: 210 B/F
Secondary topics:  Best Practice, Framework-focused
Derek Ferguson (Fitch Solutions), Laura Shornack (JPMorgan Chase)
Average rating: ****.
(4.33, 3 ratings)
Private clouds present many unique challenges to architects and software engineering wishing to build and deploy machine learning solutions. Derek Ferguson and Laura Schornack walk you through a real-world example that addresses these challenges using Kubernetes, TensorFlow, and KubeFlow. Read more.
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4:50pm–5:35pm Thursday, June 13, 2019
Serverless architecture for data science
Location: 210 A/E
Secondary topics:  Best Practice, Framework-focused, Hands-on
Rustem Feyzkhanov (Instrumental)
Average rating: ***..
(3.67, 3 ratings)
One of the main issues with deploying deep learning solutions is finding the right way to operationalize models within the company. The serverless approach for deep learning provides cheap, simple, scalable, and reliable architecture. Rustem Feyzkhanov shows you how to deploy the TensorFlow model for image captioning on AWS infrastructure. Read more.
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4:50pm–5:35pm Thursday, June 13, 2019
An architecture for building secure and privacy preserving ML products
Location: 212
Secondary topics:  Overview
LN Renganarayana (Workday)
LN Renganarayana is on a mission to provide strong privacy and security for ML products built with customer data. A key enabler of Workday's mission is an architecture guided by the principles of privacy by design and data protection by default. Interested? Come learn about the design and the trade-offs. Read more.