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MLOps in Practice: Hyperparameter Optimization with MLflow
Hyperparameter optimization is a bread-and-butter task for data scientists and machine-learning engineers; basically every model-development project requires it. One particular challenge in hyperparameter optimization is tracking the sheer number of experiments.

Enter MLflow. MLflow serves a handful of important purposes in machine-learning projects – environment management, streamlining of deployments, artifact persistence – but in the context of hyperparameter optimization, it is particularly useful for experiment tracking. A good MLOps pipeline enables reproducible research by keeping track of experiments automatically so that data scientists can focus on innovation.

In this webinar, we will cover:
- How MLOps enables models to move through their life cycle more efficiently, and how MLFlow addresses those needs
- Why experiment tracking in the data science process is vital to MLOps success
- How using MLflow makes it easy for data scientists to diligently log experiments with minimal overhead
- How to integrate MLflow experiment tracking into data science workflows using the Hyperopt package for hyperparameter optimization at scale on Apache Spark

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Oct 21, 2020 01:00 PM in Central Time (US and Canada)

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Dominick Rocco
Data Scientist @phData
Dominick currently works at phData specializing in reproducible data-science research and development of production-grade machine-learning pipelines. He spent over three years working on developing machine-learning applications to make hospitals run more efficiently with 3M Health Information Systems. Prior to that, Dominick completed his dissertation in experimental particle physics, which focused on developing one of the first deep-learning algorithms used in that field.
Maggie Chu
Partner Solutions Architect @Databricks
Maggie started out her startup tech career in Hong Kong and then moved into the cloud data analytics startup space in Silicon Valley. She has been working at Databricks as a Solutions Architect for over 2 years, delivering and implementing solutions for customers in the SF bay area. Maggie is currently focusing on helping our consulting and SI partners become successful with client projects.