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.// MLOps Jobs board  jobs.mlops.community// MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links--------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/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-umdTimestamps:[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