Griffin, ML Platform at Instacart // Sahil Khanna // MLOps Podcast #145

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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// Related Links

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/
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