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Architectural and Organizational Patterns in Machine Learning with Nishan Subedi - #462

Architectural and Organizational Patterns in Machine Learning with Nishan Subedi - #462

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)


Architectural and Organizational Patterns in Machine Learning with Nishan Subedi - #462

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

ratings:
Length:
58 minutes
Released:
Mar 8, 2021
Format:
Podcast episode

Description

Today we’re joined by Nishan Subedi, VP of Algorithms at Overstock.com. In our conversation with Nishan, we discuss his interesting path to MLOps and how ML/AI is used at Overstock, primarily for search/recommendations and marketing/advertisement use cases. We spend a great deal of time exploring machine learning architecture and architectural patterns, how he perceives the differences between architectural patterns and algorithms, and emergent architectural patterns that standards have not yet been set for. Finally, we discuss how the idea of anti-patterns was innovative in early design pattern thinking and if those concepts are transferable to ML, if architectural patterns will bleed over into organizational patterns and culture, and Nishan introduces us to the concept of Squads within an organizational structure. The complete show notes for this episode can be found at https://twimlai.com/go/462.
Released:
Mar 8, 2021
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.