Model Context Sensitivity
Sharath discusses the balance between context sensitivity and latency in model recommendations, emphasizing the importance of understanding what information is necessary for effective predictions. He explains how models can continuously score data in the background, allowing for timely recommendations while optimizing for performance. The conversation highlights the design patterns developed at Instacart to navigate these challenges effectively.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Web Scale Engineering for Machine Learning with Sharath Rao - #40
Related Questions
Can you expand on smart recommendation systems in machine learning?
How do you leverage different models in machine learning in the episode Web Scale Engineering for Machine Learning with Sharath Rao - #40 and the clip Real-Time Recommendations?
Can you expand on smart recommendation systems in the fashion industry as discussed in the episode Web Scale Engineering for Machine Learning with Sharath Rao - #40 and the clip Model Context Sensitivity?