
Paul Zikopoulos39









#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

Elaine Hanley
A key aspect of #DataOps is instrumentation - which allows ROI to be effectively measured
(edited)

Julie Lockner
#DataOps optimizes #DataScientist skills andtime

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.

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.

Chris Bergh
DataOps is more than just for Data Scientists -- Data Engineers, Analysts, BU, Governance or anyone in the data value chain

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.

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

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, +++