MLOps Coffee Sessions #145 with Sahil Khanna, Griffin, ML platform at Instacart, co-hosted by Mike Del Balso.
// Abstract
The conversation revolves around the journey of Instacart in implementing machine learning, starting from batch processing to real-time processing. The speaker highlights the importance of real-time processing for businesses and the relevance of Instacart's journey to other machine learning teams.
Sahil emphasizes the soft factors, such as staying customer-focused and the right approach, that contributed to the success of Instacart's machine learning implementation. We also recommend two blog posts by Sahil about Instacart's journey.
// Bio
Sahil is currently a machine learning engineer at Instacart, where they are building a centralized platform for the training, deployment, and management of diverse ML applications. Before Instacart, Sahil developed ML training and inference platforms at Etsy.
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Connect with Sahil on LinkedIn: www.linkedin.com/in/sahil-khanna-umd
Timestamps:
[00:00] Sahil's preferred coffee
[01:35] Introduction to Sahil Khanna
[01:59] Takeaways
[08:07] Subscribe to our Newsletter and join our In Real Life Meetups around 30 cities in the world!
[09:25] Learning how to make Pizza and Focaccia
[10:45] Batch prediction style to real-time
[13:15] High-Level MLOps Context Determination
[17:00] 2 kinds of ML Platform
[20:12] The Dilemma of Rapidly Evolving Requirements
[24:31] Targeting the Right User: Understanding the ML Platform Team's Customers
[25:29] Interesting journey
[27:18] Griffin
[31:31] Docker base components, a unified interface, and extensible sections
[31:50] Navigating the challenges across Consistent Development Environments
[36:30] Feature management
[38:33] Stages in adopting real-time ML
[41:06] On-demand features
[42:21] Future of streaming
[44:00] Sessions featurization
[47:27] Buying third-party products from the engineer and vendor side
[50:11] Modular Dependency Integration
[51:46] Wrap up
