Autologging and MLFlow in Fabric Data Science

🔍 Autologging revolutionizes how we track MLFlow runs by significantly reducing the code needed to log parameters, metrics, and model artifacts during training. This episode delves into how this feature extends MLflow's autologging capabilities and seamlessly integrates into the Synapse Data Science experience in Microsoft Fabric.
👩‍💻 For developers and data scientists, this means an effortless way to track and compare model performances, eliminating the hassle of manual tracking. Join us as Misha Desai unveils the intricacies of autologging, offering insights into its development, functionality, and impact on machine learning workflows.
📊 Whether you're fine-tuning your machine learning models or just starting in data science, this episode is a treasure trove of information on leveraging autologging for optimal performance and efficiency.
🎙 Meet the Speakers:
👤 Guest from Microsoft Fabric Product Group: Misha Desai, Senior Program Manager
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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