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Learn best practices for training models and managing experiments, projects and models using MLflow

Data teams face numerous challenges throughout the data science and machine learning lifecycle that hinder productivity. As organizations continue to become more data-driven, a collaborative environment that supports the full machine learning lifecycle becomes increasingly critical — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.

Join Databricks for this follow-along session where you’ll get access to their open and collaborative platform and learn how it simplifies data science by enabling you to run all types of machine learning workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Register here

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