Building a Centralized Background for Seamless User Experience

MLOps.community · Intermediate ·📐 ML Fundamentals ·3y ago
Sahil discusses how they had many tools and libraries in place to provide different abilities, but are now building a centralized background for a seamless user experience. The vision is for the user to be able to define the features they want to use, and if it exists, they can use it. If not, they can submit a job to compute those features. The user can then train, publish, and deploy their model to production. Previously, their tools were for customized applications where users could write their own code, but they are now moving towards providing standard applications where users can provide configuration, and the team will deploy that configuration to their standard code for computing. The goal is to streamline the user experience and make it easier to deploy models to production. MLOps Coffee Sessions #145 with Sahil Khanna, Griffin, ML platform at Instacart co-hosted by Mike Del Balso. Link to the full episode: https://youtu.be/iMizuHVPX0M // 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 https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https:/
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 48 Adjacent usecases and multistep feature engineering
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