Model & Experiment Tracking in Fabric Data Science (MLFLOW)

🔍 Microsoft Fabric offers Data Science experiences to empower users to complete end-to-end data science workflows for the purpose of data enrichment and business insights. You can complete a wide range of activities across the entire data science process, all the way from data exploration, preparation and cleansing to experimentation, modeling, model scoring and serving of predictive insights to BI reports.

With tools like PySpark/Python, SparklyR/R, Microsoft Fabric Notebooks can handle machine learning model training. ML algorithms and libraries can help train machine learning models. Library management tools can install these libraries and algorithms. Users have therefore the option to leverage a large variety of popular machine learning libraries to complete their ML model training in Microsoft Fabric.

MLflow experiments and runs can track the ML model training. Microsoft Fabric offers a built-in MLflow experience with which users can interact, to log experiments and models. Learn more about how to use MLflow to track experiments and manage models in Microsoft Fabric.

🎙 Meet the Speakers:

👤 Guest from Microsoft Fabric Product Group: Misha Desai, Senior Program Manager at Microsoft



Misha is a Senior Product Manager based in Seattle, WA, specializing in model tracking, training, and governance within the Fabric Data Science team.

Linkedin: www.linkedin.com/in/misha-desai-6034a362



👤 Host: Estera Kot, Senior Product Manager at Microsoft and a member of the Fabric Product Group. She holds the role of Product Owner for Apache Spark-based runtimes in Microsoft Fabric and Synapse Analytics. Estera is a Data & AI Architect and is passionate about computer science.
LinkedIn:   / esterakot  
Twitter:   / estera_kot  

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