16 avsnitt

Welcome to Data Brew by Databricks with Denny and Brooke! In this series, we explore various topics in the data and AI community and interview subject matter experts in data engineering/data science. So join us with your morning brew in hand and get ready to dive deep into data + AI! For this first season, we will be focusing on lakehouses – combining the key features of data warehouses, such as ACID transactions, with the scalability of data lakes, directly against low-cost object stores.

Data Brew by Databricks Databricks

    • Teknologi

Welcome to Data Brew by Databricks with Denny and Brooke! In this series, we explore various topics in the data and AI community and interview subject matter experts in data engineering/data science. So join us with your morning brew in hand and get ready to dive deep into data + AI! For this first season, we will be focusing on lakehouses – combining the key features of data warehouses, such as ACID transactions, with the scalability of data lakes, directly against low-cost object stores.

    Data Brew Season 3 Episode 1: Disrupt: Challenge your Business Assumptions

    Data Brew Season 3 Episode 1: Disrupt: Challenge your Business Assumptions

    For our third season, we focus on how leaders use data for change. Whether it’s building data teams or using data as a constructive catalyst, we interview subject matter experts from industry to dive deeper into these topics.

    In this season opener, Elena Donio shares her experience using data and domain knowledge to disrupt the traditional service and sales compensation model. She also discusses how to build companies that scale, manage corporate cultural evolution, and the influence of corporate boards.

    See more at databricks.com/data-brew

    • 29 min
    Data Brew Season 2 Episode 9: Data Driven Software

    Data Brew Season 2 Episode 9: Data Driven Software

    For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.

    We branch, version, and test our code, but what if we treated data like code? Tim Hunter joins us to discuss the open-source Data-Driven Software (DDS) package and how it leads to immense gains in collaboration and decreased runtime for data scientists at any organization.

    See more at databricks.com/data-brew

    • 31 min
    Data Brew Season 2 Episode 8: Feature Engineering

    Data Brew Season 2 Episode 8: Feature Engineering

    For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.

    Is there ever a “one-size fits all” approach for feature engineering? Find out this and more with Amanda Casari and Alice Zheng, co-authors of the Feature Engineering for Machine Learning book.

    See more at databricks.com/data-brew

    • 31 min
    Data Brew Season 2 Episode 7: Interpretable Machine Learning

    Data Brew Season 2 Episode 7: Interpretable Machine Learning

    For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.

    What does it mean for a model to be “interpretable”? Ameet Talwalkar shares his thoughts on IML (Interpretable Machine Learning), how it relates to data privacy and fairness, and his research in this field.

    See more at databricks.com/data-brew

    • 37 min
    Data Brew Season 2 Episode 6: AutoML

    Data Brew Season 2 Episode 6: AutoML

    For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.

    Erin LeDell shares valuable insight on AutoML, what problems are best solved by it, its current limitations, and her thoughts on the future of AutoML. We also discuss founding and growing the Women in Machine Learning and Data Science (WiMLDS) non-profit.

    See more at databricks.com/data-brew

    • 35 min
    Data Brew Season 2 Episode 5: ML Applications

    Data Brew Season 2 Episode 5: ML Applications

    For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.

    Good machine learning starts with high quality data. Irina Malkova shares her experience managing and ensuring high-fidelity data, developing custom metrics to satisfy business needs, and discusses how to improve internal decision making processes.

    See more at databricks.com/data-brew

    • 32 min

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