
Sign up to save your podcasts
Or


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.
By Sam Charrington4.7
422422 ratings
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.

1,105 Listeners

168 Listeners

305 Listeners

345 Listeners

233 Listeners

209 Listeners

205 Listeners

314 Listeners

100 Listeners

552 Listeners

148 Listeners

102 Listeners

229 Listeners

688 Listeners

34 Listeners