analytics - Machine-learning Overview -


this may not type of question ask on so, wanted hear other people have regarding factors consider in implementing machine-learning algorithms in large enterprise environment.

one of goals research industry machine-learning solutions can tailored company's specific needs. being pretty person has math background on team , and has done background reading on machine-learning algorithms previously, i'm tasked explaining/comparing machine-learning solutions in industry. i've gleaned googling around, seems that:

a. machine-learning , predictive analytics aren't same thing, what's inherently different when company offers predictive analytics software vs. machine-learning software? (e.g. ibm predictive analytics vs. skytree server)

b. lot of popular terminology gets entangled together, regarding big data, hadoop, machine-learning, etc. clarify distinction among terms? i've learned, think conceptual separation goes like:

  • machine-learning algorithms
  • software implementation
  • infrastructure run software on large datasets (hadoop)

c. when implementing solution, companies hire consultants solution company implement algorithms, or algorithms pre-built , data analyst can use them? or need team of data scientists, software, run algorithms , understand output?

i know quite long-winded question(s), info helpful. it's kind of difficult being person remotely knows stuff, i'd love hear more experienced , technical people have say.

it's hard answer question without idea of how data have , company's needs are. narrow down types of solutions can meet needs. among those, there open source solutions (mahout perhaps), visualization solutions, , variety of solutions manage data.


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