Data Brew Season 2 Episode 8: Feature Engineering

Data Brew by Databricks

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cách đây 4 năm trước 31:17

MP3•Trang chủ episode

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

42 tập

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

Data Brew by Databricks

Data Brew Season 2 Episode 8: Feature Engineering

Data Brew by Databricks

71 subscribers

published cách đây 4 năm trước

Chia sẻ

MP3•Trang chủ episode

42 tập

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

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Data Brew by Databricks

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Data Brew Season 4 Episode 6: Professional Athletes 35:49

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Data Brew Season 4 Episode 5: Public Health: Education, Access, and Policy 34:39

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Data Brew Season 3 Episode 6: Open Source 33:49

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Data Brew Season 3 Episode 5: Sustainability & Sake 32:26

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Data Brew Season 3 Episode 4: Executive Education 38:46

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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. Did you know that the average tenure of a board member is longer than the average tenure of a marriage in the United States? In this episode, Coco Brown discusses the benefits and drawbacks of the long tenures of corporate boards, their current structure, the impact of recent legislation, and the importance of executive education to guide you through all of this. See more at databricks.com/data-brew…

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Data Brew by Databricks

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Data Brew Season 3 Episode 1: Disrupt: Challenge your Business Assumptions 29:45

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Data Brew Season 2 Episode 9: Data Driven Software 31:12

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Data Brew by Databricks

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Data Brew Season 2 Episode 8: Feature Engineering 31:17

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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…

Data Brew by Databricks

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Data Brew Season 2 Episode 7: Interpretable Machine Learning 37:07

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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…

Data Brew by Databricks

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Data Brew Season 2 Episode 6: AutoML 35:55

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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…

Data Brew by Databricks

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Data Brew Season 2 Episode 5: ML Applications 32:40

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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…

Data Brew by Databricks

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Data Brew Season 2 Episode 4: Hyperparameter and Neural Architecture Search 33:25

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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. Liam Li is a leading researcher in the fields of hyperparameter optimization and neural architecture search, and is the author of the seminal Hyperband paper. In this session, Liam discusses the evolution of hyperparameter optimization techniques and illustrates how every data scientist can benefit from neural architecture search. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 2 Episode 3: Infrastructure for ML 30:34

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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. Adam Oliner discusses how to design your infrastructure to support ML, from integration tests to glue code, the importance of iteration, and centralized vs decentralized data science teams. He provides valuable advice for companies investing in ML and crucial lessons he’s learned from founding two companies. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 2 Episode 2: Data Ethics 25:47

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Data Brew by Databricks

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Data Brew Season 2 Episode 1: ML in Production 30:49

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For our second season, 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. In the season opener, Matei Zaharia discusses how he entered the field of ML, best practices for productionizing ML pipelines, leveraging MLflow & the Lakehouse architecture for reproducible ML, and his current research in this field. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 1 Episode 6: Journey of Big Data 40:16

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Jules Damji and Tathagata Das guide us through their journey in big data and the evolution of data architecture in the past 30 years. They discuss some of the biggest changes in industry they’ve seen, as well as trends to look forward to in the coming years. This is a fun episode connecting all four authors of the Learning Spark, 2nd Edition book. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 1 Episode 5: Combining Machine Learning and MLflow with your Lakehouse 36:00

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Ellissa Verseput , ML Engineer at Quby, joins Denny and Brooke to discuss how Quby leverages ML to extract additional value from their data lake and how they manage this process. See more at databricks.com/data-brew

Data Brew by Databricks

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Data Brew Season 1 Episode 4: BI on Data Lakes - Making it Real for Retail 29:05

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In this session, we discuss the lessons learned with Lara Minor, Senior Enterprise Data Manager at Columbia Sportswear, on how her team achieved a 70% reduction in pipeline creation time. This had reduced ETL workload times from four hours with previous data warehouses to minutes enabling near real-time analytics. Her team migrated from multiple legacy data warehouses, run by individual lines of business, to a single scalable, reliable, performant data lake. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 1 Episode 3: Demystifying Delta Lake 25:51

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Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs. For our “Demystifying Delta Lake” session, we will interview Michael Armbrust - committer and PMC member of Apache Spark™ and the original creator of Spark SQL. He currently leads the team at Databricks that designed and built Structured Streaming and Delta Lake. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 1 Episode 2: Welcome to Lakehouse 26:10

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Legacy approaches have failed to deliver on the promise of a single data architecture that can support every downstream use case from BI to AI. Lakehouse aspires to address this by combining the best of data warehouses and data lakes. Ali Ghodsi, Co-Founder and CEO of Databricks, and David Meyer, SVP of Product at Databricks, explain how. See more at databricks.com/data-brew…

Data Brew by Databricks

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Data Brew Season 1 Episode 1: From data warehousing to data lakes in 40 minutes 44:48

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In our inaugural episode, we’d like to welcome data warehouse luminaries Barry Devlin, Susan O’Connell, and Donald Farmer to discuss the evolution of data warehouses, data lakes, and lakehouses. See more at databricks.com/data-brew

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