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JuliaCon 2020 | Enterprise data management with low-rank topic models | Jiahao Chen
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Abstract:
Description:
To adopt modern practices for reproducible data science, enterprises need to first know what kinds of data they have. In some industries like financial services, being able to reproduce critical risk calculations is even a regulatory requirement. A necessary first step is for enterprises to build comprehensive data catalogue, before building other infrastructure such as data lakes. Building such a catalogue can be challenging for enterprises with multiple legacy systems, incomplete documentation, and inherited technical debt. The expert knowledge needed to provide and verify subject labels further escalates the cost of building a data catalogue.
00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Description:
To adopt modern practices for reproducible data science, enterprises need to first know what kinds of data they have. In some industries like financial services, being able to reproduce critical risk calculations is even a regulatory requirement. A necessary first step is for enterprises to build comprehensive data catalogue, before building other infrastructure such as data lakes. Building such a catalogue can be challenging for enterprises with multiple legacy systems, incomplete documentation, and inherited technical debt. The expert knowledge needed to provide and verify subject labels further escalates the cost of building a data catalogue.
00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
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JuliaCon 2020 | Enterprise data management with low-rank topic models | Jiahao Chen
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