datasciencedevops

Anybody have any thoughts on this?

devops is the translation of "lean" into the IT world.

@plburris Really? I consider "DevOps" as the industrialization of the app development/release pipeline.

Lots of ways to "industrialize" appdev/release pipeline.

DevOps is defined by experts in the field, which isn't me, but I see it as Agile, Fail-fast, Tight-coupling between dev and ops teams, focused on rapid POV's (Proofs of Value) to meet end-user requirements

how about the intersection of application development, ITOps, and programmable infrastructure

practice of continuous innovation and improvement where each iteration builds on the next to create world-leading technology

but only a few focus on "reduce waste according to customer value."

#devops is modern software delivery methodology

@KirkDBorne Tight coupling of Dev & Ops, yes. With a strong emphasis on seamless automated handoff of production-ready code.

#devops is providing the speed, quality and innovation in software delivery that is demanded by the business

I like this DevOps graphic: http://www.agilebudd...

#DevOps is the union of people, process and tools to enable continuous delivery of value to end users

@ggilbert41 Programmable infrastructure? Perhaps. Policy-driven release governance over the lifecycle.

@furrier I'm not sure that continual refinement of the app is central to DevOps. It's more a matter of continuous automation in the dev, test, deployment, and assessment pipeline.

Shout out to my @BoozAllen #DevOps colleagues who published the Enterprise DevOps Playbook: https://www.boozalle...

Next question coming. Refresh your browsers.

@KirkDBorne That's a good one. Thanks for sharing it!

@KirkDBorne That's a good one. It's about bringing industrial-grade automation, predictabilty, and speed to the Dev-to-Ops handoff.

Its about bringing the world of Data Science into the world of integration into systems of record and the requirements of high availability, regression testing, provenance, reproducibility, & predictable performance: a collision of worlds!

@dfloyer Yes David Floyer is here

This is a loaded question but I'd say it depends where the conversation is started in organization or C-Level

it doesn't matter where the initiative starts but where it ends. Adoption should yield results

Okay, I am going to cheat. Here are the results of a recent survey on DevOps adoption: https://betanews.com...

I still think the chasm is being crossed as we speak and #devops pioneers still view devops as devops; mainstream call is #cloudOps

@KirkDBorne Great article. Thanks for sharing, Kirk.

Company putting out a manifesto doesn't make it #devops real agility and proof points wins the day

35% of some projects proves my thought on chasm crossing #cloudops is here which is #devops made easy more automation required

Like most complex, social changes: It's selective.

I have not seen any research pointing to DevOps adoption rates in enterprise data science. but this cited research gives numbers on DBA adoption of DevOps, which is interesting.

Question #4 up above.

#devops challenge is scaling it beyond the small number of teams and projects

Where Agile is the dev process, and ops process less driven by hardware (i.e., cloud), more likely to find devops.

main comment from #devops pros is: "devops is never finished"

I think incentives across siloed executive leadership are the largest inhibitors to the DevOps transformation; once executive incentives motivate collaboration over siloed transformations, then you win

There are various definitions of DataOps, but I prefer this one: #DevOps for #DataScience = #DataOps (IMHO). It's about #DataProduct design, development, deployment lifecycle.

We (@petrguerra @boozallen and I) did talk about #DataOps here: http://www.oreilly.c...

This also might be good to talk value stream automation

Only when development perceive that the Ops is useful in getting code out faster.

@furrier Another key challenge is scaling to handle the growing range of artifacts--code snippets, statistical models, metadata, schemas, etc.--in a complex app-dev pipeline.

The rate of change (actually, rate of acceleration) in digital transformation is really high and is jerking biz around (including tipping points and future shock): https://www.amazon.c... by @csurdak

@KirkDBorne We equate digibiz = differential use of data. As more firms institutionalize work around data assets, more digibiz -- which amplifies the role of data assets.

@KirkDBorne Hence, the acceleration.

data science!

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

#devops enablers are integrated "open toolchains"

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

#devops enablement #3: microservices and container adoption

#devops enabler #4: fully integrated #PrivateCloud and #PublicCloud

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

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

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

Question 6 coming.

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

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

#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/...

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

@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.

@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.

@KirkDBorne Merci!

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

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