
jameskobielus34


















Peter Burris
data science!

Peter Burris
I know it sounds recursive, but devops is going to need a LOT of data science-like stuff to reach full potential.

John Furrier
#devops enablers are integrated "open toolchains"

John Furrier
#devops enable #2: use of analytics and cognitive/deep learning

John Furrier
#devops enablement #3: microservices and container adoption

jameskobielus
I'll jump in here: source-control repository, data lake, and integrated collaboration environment that spans the entire pipeline.

John Furrier
Just did a #crowdchatstorm in a thread take that @pmarca

jameskobielus
@furrier The integrated toolchain needs to be embedded within the integrated DevOps collaboration environment that I alluded to.

jameskobielus
Question 6 coming.

Kirk Borne
#Microservices come to mind. Also, APIs... here are just a few: https://twitter.com/...

Kirk Borne
#Containers have really had a huge positive impact on productivity for #MachineLearning #DataScience teams in my organization

Kirk Borne
#DataLakes done right will definitely be a big plus in breaking down data silos, enabling rapid innovation, and zero-day discovery from new data sources: https://twitter.com/...

Peter Burris
@KirkDBorne How? I can see why it might, but can you offer specifics?

jameskobielus
@plburris Right. Automated ML-driven code-gen tools are coming along fast and furious. Microsoft, for example, is making great strides to use ML for rapid app development and iteration.

Kirk Borne
@plburris Schema-on-read is fast. Schema-on-write requires months of data modeling, design, development, testing,... i.e., DevOps in the database-build phase, yes, but which I am not seeing too much of.

Peter Burris
@KirkDBorne Merci!

Kirk Borne
See my article "Mining the #BigData Wheel" that mentions fast schema-on-read day-zero analytics here: https://mapr.com/blo... at @MapR #DataScience

jameskobielus
@KirkDBorne You are quite right. Precious little DevOps drives the database modeling process.