<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Feature engineering – M365.FM</title><link>https://www.m365.fm/entity/feature-engineering/</link><description>Feature engineering is the process of transforming raw data into input variables (features) for machine learning models, for example through cleaning, aggregation and encoding.</description><language>en</language><item><title>Accelerating Data Science Workflows with Azure Open Datasets</title><link>https://www.m365.fm/community/u/mirko-p/blog/accelerating-data-science-workflows-with-azure-open-datasets/</link><guid>https://www.m365.fm/community/u/mirko-p/blog/accelerating-data-science-workflows-with-azure-open-datasets/</guid><pubDate>Thu, 27 Aug 2026 18:39:32 -0000</pubDate><category>article</category><description>Welcome back to the podcast companion blog! As data scientists and analytics engineers, we spend an astronomical amount of our valuable time tracking down, cleaning…</description></item><item><title>Production-Grade Notebooks: Guardrails and Git Integration</title><link>https://www.m365.fm/community/u/mirko-p/blog/production-grade-notebooks-guardrails-and-git-integration/</link><guid>https://www.m365.fm/community/u/mirko-p/blog/production-grade-notebooks-guardrails-and-git-integration/</guid><pubDate>Tue, 11 Aug 2026 19:33:03 -0000</pubDate><category>article</category><description>Welcome back to the podcast companion blog! In this post, we are diving deep into how you can move beyond ad-hoc scripting by treating your Fabric notebooks like pr…</description></item><item><title>Use Fabric Notebooks for Data Transformation and ML</title><link>https://www.m365.fm/show/episodes/fabric-notebooks-for-data-transformation-and-ml/</link><guid>https://www.m365.fm/show/episodes/fabric-notebooks-for-data-transformation-and-ml/</guid><pubDate>Sat, 09 Aug 2025 00:00:00 -0000</pubDate><category>episode</category><description>Stop wrestling Power BI with endless clicks. Microsoft Fabric Notebooks let you prep, model, and feature-engineer data end-to-end—in Python or R—right inside your Lakehouse with Spark scale and Git governance. This guide shows a proven workflow: ingest to…</description></item></channel></rss>