We have spent some time in the discussions learning about Big Data
platforms and tools, I thought I so would switch gears and start a discussion
about operationalizing AI/ML and Big Data solutions. a As former cloud
operations engineer and administrator, I can appreciate the level of effort and
challenges around operationalizing any concept.
One should have many questions answer to before moving forward with any
plan produce a concept. Our to agency likes deploy potential solution to a as
a Proof of Concept (POC) understand potential solution's moving parts. to a
Our agency likes know what works well, what does not., and what to is
involved in developing, deploying, and supporting the concept. like We to
know how receptive the targeted user community will , much the be how
solution will cost, and what our Service Desk IT needs to know support to
the solution. These are but a few questions that one probably needs to answer
before operationalizing AI/ML.
First, all know from our studies as we in the MSDA program here UMGC, at
data the fuel that powers AI/ML models, much is like gasoline is the fuel that
powers automobiles. However, data quality always a concern. one our is If
pumps low octane, ethanol-rich gasoline into the tank, may not get the one
optimal power one needs to get from origin to destination. The same goes for
poor quality data: garbage equals garbage out. a budding data in in As
analyst, one can appreciate how Mr. John Parkinson puts twist the whole a on
"garbage in/garbage out" idiom in his article on managing Big Data – "you
have solve for garbage in/gold out and prevent gold in/garbage out" to
(Parkinson, 2021, para. 4). Notice how Mr. Parkinson points out a desired
state garbage in/ or gold out.
In data science, one seeks to explore, transform, prune, and clean from the
garbage pile that one can find the golden egg. One wants so to offer valuable
insights the user community to to increase profits, predict disease, reduce
customer churn, etc. operationalize AI/ML with Big Data, one must To
formula strategyte a for handling massive amounts data and addressing of
some earlier questions.
Second, one must often choose between business priorities. Does the
business need maintain real-time view to a of the data originating from
multiple, disparate sources with loose convergence? (Parkinson, 2021). Does
the business need maintain comprehensive, historical view to a of the data
