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For the past 1.5 years, the Microsoft Fabric Product Group Product Managers have been publishing a YouTube series featuring deep dives into Microsoft Fabric's features. These episodes cover both technical functionalities and real-world scenarios, providing insights into the product roadmap and the people driving innovation. With over 80+ episodes, the series serves as a valuable resource for anyone looking to understand and optimize their use of Microsoft Fabric.
All episodes are available at https://aka.ms/fabric-espresso making it easy to explore the entire catalog. However, to enhance the learning experience, we are launching a short series of blog posts that will categorize the episodes into thematic groups, providing explanations and key takeaways for each.
This week, we focus on Data Science & Machine Learning in Microsoft Fabric. Below is a curated list of episodes that explore various aspects of data science workflows, model training, AutoML, and machine learning capabilities in Fabric.
1. Low Code AutoML UI in Microsoft Fabric Data Science
2. Hyperparameter tuning in Fabric Data Science
3. AutoML in Fabric Data Science – automated model training and optimization
4. Data Wrangler in Microsoft Fabric (an immersive interface for exploratory data analysis)
5. Microsoft Fabric Data Science – Detecting Machine Failure (Classification)
6. Fabric Data Science Cust churn scenario – when a customer will stop doing business with the bank
7. Fabric Data Science Sales Forecasting: Predict sales numbers for product categories at a superstore
8. Autologging and MLFlow in Fabric Data Science
9. Model & Experiment Tracking in Fabric Data Science (MLFLOW)
10. R (Programming Language) in Microsoft Fabric – An Insightful Overview with Fabric Product Group
11. Data Science in Microsoft Fabric – Model scoring with PREDICT
12. Data Science in Microsoft Fabric – Data preparation and exploration with Data Wrangler
13. Fabric Copilot billing pricing
14. Copilot in Microsoft Fabric | Ultimate demo of copilots in Notebooks – DS & DE
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