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#DataOps is a requirement to get the ROI on your data science teams ... you'll never get the ROI of a data scientists' high salary unless you take away the time they spend on DataOps & put it into a focussed service: data science is a team sport & this team needs a DataOps player
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Elaine Hanley
A key aspect of #DataOps is instrumentation - which allows ROI to be effectively measured
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Julie Lockner
#DataOps optimizes #DataScientist skills andtime
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Dave Vellante
totally agree - wrangling data gets old - even for data hackers...
Paige Bartley
@julielockner Our research is suggesting that data scientists are spending nearly half their time just finding and preparing data. If you can streamline that process via #dataops, you can immensely boost productivity.
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Paul Zikopoulos
Yup! I need my data scientists search the hyper parameter space for better model performance; give them a shop for data approach with lineage and service-based flows that do the work for me.
Bradley Shimmin
It seems that what we're asking for here is perhaps something akin to expanding AutoML to encapsulate data prep.
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Chris Bergh
DataOps is more than just for Data Scientists -- Data Engineers, Analysts, BU, Governance or anyone in the data value chain
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Jay Limburn 🇪🇺
An intelligent catalog that provides that self service, automates the governance and turns the output of the governance initiative into insight for the data scientists is at the heart of a good #dataops strategy.
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David Menninger
@julielockner ditto. Our research shows accessing and preparing data is the most common challenge faced in applying #AI/#ML https://www.crowdchat.net/s/260a0
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Paul Zikopoulos
So there is surely a part of this that can connect - AutoML will look at the data and suggest some algorithms, but also great AutoML platforms inspect the data, report on it, fix it (suggest), identify, record metadata, +++