Ben Sadeghi is a Partner Solutions Architect at Databricks, covering Asia Pacific and Japan, focusing on Microsoft and its partner ecosystem. Having spent several years with Microsoft as a Big Data & Advanced Analytics Technology Specialist, he has helped various companies and partners implement cloud-based, data-driven, machine learning solutions on the Azure platform.Prior to Databricks and Microsoft, Ben was engaged as a data scientist with Hadoop/Spark distributor MapR Technologies (APAC), developed internal and external data products at Wego, a travel meta-search site, and worked in the Internet of Things domain at Jawbone, where he implemented analytics and predictive applications for the UP Band physical activity monitor. Before moving to the private sector, Ben contributed to several NASA and JAXA space missions.Ben is an active member of the open-source Julia language community. He holds an M.Sc. in computational physics, with an astrophysics emphasis.View the profile
About the talk
MLflow is an open-source platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). In this talk, we'll discuss MLflow's components and run through a quick demo.
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