<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>platform on Vaishnav Mhetre</title><link>https://vaishnavmhetre.github.io/tags/platform/</link><description>Recent content in platform on Vaishnav Mhetre</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Thu, 24 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://vaishnavmhetre.github.io/tags/platform/index.xml" rel="self" type="application/rss+xml"/><item><title>Foundations for AI-driven initiatives</title><link>https://vaishnavmhetre.github.io/case-studies/ai-initiatives/</link><pubDate>Thu, 24 Sep 2026 00:00:00 +0000</pubDate><guid>https://vaishnavmhetre.github.io/case-studies/ai-initiatives/</guid><description>A case study about engineering the data and delivery foundations that AI-driven initiatives depend on — reusing the pipeline, not rebuilding bespoke paths.</description></item><item><title>Migrating prediction services onto the delivery pipeline</title><link>https://vaishnavmhetre.github.io/case-studies/prediction-services-migration/</link><pubDate>Thu, 24 Sep 2026 00:00:00 +0000</pubDate><guid>https://vaishnavmhetre.github.io/case-studies/prediction-services-migration/</guid><description>A case study about supervising the migration of prediction services — such as a pricing forecast backend — onto a shared delivery pipeline, and making them robust.</description></item></channel></rss>