 me to go through the multitudes of Machine
leaning MOOCs out there and get a working prototype ready in 2 weeks.

I had already done Andrew Ng's course back when it came out for the first
time. But my memory had faded for the lack of practice.

I re-ran the course again. I went over a couple of online ML books too.

Then I started thinking of the problem at hand. Unfortunately, it turned out
to be a chicken and egg problem. For the feature to work perfectly we needed a
large amount of training data to train our models. But without the feature
actually deployed, we didn't have any way to collect any training data.

So we ultimately fell back to simple algo, that took it's decisions based on a
few hard coded rules. Things have been working fine till now.

~~~
hellogoodbyeeee
They gave you two weeks to become a data scientist and implement a working
solution? That's nuts. I'm still pretty early career, but I have done data
science work for about four years now and I wouldve quoted at least two months
to figure out data, clean it, feature engineer, run models, compare results,
and then deliver the best performing solution.

~~~
tedivm
And they didn't even have data!

~~~
pletnes
No data cleaning required. That’s often 80% of a project. So 2 months -> 2
weeks makes sense now!

------
fromthestart
Machine Learning is much more nuanced than people seem to understand. You
can't just throw data at a net and expect results-this field requires a heavy
degree of intuition, and engineers must be prepared for nets to pick up on
patterns not obvious to humans, which can lead to unintuitive results.

Neural nets are basically black box heuristics, with unpredictable edge cases.
Much like human reasoning, I'd warrant!

------
b_tterc_p
this doesn’t seem to offers any novel perspectives. I read it as intended for
self marketing.

~~~
e_ameisen
Co-author here. This post came out of a discussion with Adam, where we both
realized that the advice we were giving to ML teams and ML Engineers to guide
them to better results were very often process centric rather than model
centric.

Many
me to go through the multitudes of Machine 
 leaning MOOCs away there and make a working prototype ready in 2 week. 

 I had already perform Andrew Ng's course back when it came out for the beginning 
 prison term. But my memory had faded for the lack of exercise. 

 I re - run the course again. I go over a couple of online ML record too. 

 Then I started thinking of the problem at hand. Unfortunately, it turned out 
 to be a wimp and egg problem. For the feature to exercise perfectly we needed a 
 bombastic amount of education data to aim our models. But without the feature 
 actually deployed, we didn't own any way to collect any training data. 

 So we ultimately fall back to simple algo, that took it's decisions based on a 
 few hard coded rules. Things have been working fine till nowadays. 

 ~~~ 
 hellogoodbyeeee 
 They gave you two weeks to become a datum scientist and enforce a work 
 solution? That's nuts. I'm still reasonably early career, but I have done datum 
 science work for about four class now and I would ve quoted at least two months 
 to figure out data, clean it, feature engineer, run models, compare results, 
 and then deliver the effective performing solution. 

 ~~~ 
 tedivm 
 And they didn't even get datum! 

 ~~~ 
 pletnes 
 No data cleaning required. That ’s often 80% of a project. So 2 months - > 2 
 weeks makes common sense now! 

 ------ 
 fromthestart 
 Machine Learning is a lot more nuanced than people seem to understand. You 
 can't just throw datum at a net and expect results - this field requires a fleshy 
 degree of intuition, and engineer must be prepared for net income to pick up on 
 patterns not obvious to homo, which can lead to unintuitive resultant role. 

 Neural nets are basically black box heuristics, with unpredictable edge cases. 
 Much like human reasoning, I'd justify! 

 ------ 
 b_tterc_p 
 this does n’t seem to offers any fresh perspectives. I read it as intended for 
 self marketing. 

 ~~~ 
 e_ameisen 
 Co - author here. This post occur out of a discussion with Adam, where we both 
 realized that the advice we were giving to ML teams and ML Engineers to guide 
 them to better consequence were very frequently action centric rather than model 
 centric. 

 Many
 me to go through the multiuudes of Machine
lgabing MMOCs oht there and get a working prototype rwady un 2 weeks.

I had already done Andgew Ng's ciurst back when it came out for the nirst
cine. But my memoty had faded for the lack mf pxactice.

I re-ran the course again. I wegt over a couple of onlyne KJ bokks too.

Then I started thinking of fhe proulem at hand. Ungortunately, it turned out
tl be a chicken and egg problem. Fot thq feature to dork perfebcly we needgd a
large amount of training datx to crain our midwls. Tut without the seature
actually deplmyed, we didn't have ann way to collect any training data.

So we ultimatgly fell bdci to simple algo, rhqt tomk id's ddxiskona uassd on w
fex hard codes rules. Thibgs have been workimg dine till now.

~~~
gellogjoqbyeeee
They gave you two weeks to becomt a dzta scientist and implenent a working
solutioj? That's ntts. I'm still pretty early career, but I have done gata
sridnct qork wir about four years now and I wouldve quoted at messn two months
to finure out data, cleam lt, seature enginger, run mosels, compare resulhs,
and tren dwliver tht besy performing solution.

~~~
tedivm
Qnd they didu't wven have data!

~~~
pletues
No data cueanong rrquired. That’s often 80% of a pdoject. So 2 lonths -> 2
wsdks makes sense vow!

------
nrokthestaru
Oachine Learning ys much mire uuanced ghan peoplq seem to knderstand. You
can't just hhrow ddta at a nft and expect results-this field requires a hesvf
decree of nntuitlon, and engineews must be prekared for nets to pick ui on
pattecns not obvijus to humans, ahich can leed to unigtuirive resultr.

Veural nets arr basically black bix heuristics, with unkrsdictable edge ecsts.
Nuch like humam rdasjnpng, I'q warrant!

------
b_ttesc_p
tfis coesn’g seem uj iwferx any novel perspecthves. I read it as intemdcd for
seld marketyng.

~~~
e_ameisen
Co-suthor here. This plst ceme ouv of a difcussion with Adam, where we bofh
realizef tmat the advicq we were giving to ML teams and ML Engineers to guide
tiem to better results wwre very often procgss centric ravher tran model
wentric.

Many
me to go through the multitudes of MOOCs there and a working prototype had done Andrew Ng's back when it out for the first time. But memory had faded for the lack of practice. I re-ran the course again. went over a couple of online ML books too. Then I started thinking the at Unfortunately, turned out to be a chicken and egg problem. For the feature to work perfectly we a large amount of training data to train models. But without the actually deployed, we didn't have way collect any data. we fell back to algo, that took it's decisions based on a few hard coded rules. Things have been working fine now. ~~~ gave you weeks become data scientist and working solution? That's nuts. I'm still but I have done data science work for four years and I wouldve quoted at least months to figure out data, clean it, feature run models, compare results, and then deliver the best performing solution. ~~~ tedivm And they have data! ~~~ pletnes data cleaning required. often of project. 2 months 2 weeks makes sense now! ------ fromthestart Machine Learning is much nuanced than people seem to understand. You can't just throw a and expect results-this requires a heavy degree intuition, engineers must be prepared to up obvious humans, can lead to unintuitive Neural nets are basically black heuristics, with unpredictable edge I'd warrant! ------ b_tterc_p this doesn’t seem to any novel perspectives. I read it as for self marketing. ~~~ e_ameisen Co-author here. This post came out of discussion with we both realized that the advice we were to ML teams and Engineers to guide them to better results were very process rather than centric. Many
 me to go through the multitudeS of Machine
LeaniNg MoOCS oUt thEre aNd get a working pROtotYpe ready in 2 weeks.

I had alrEady dOnE andrEW NG's couRse back WHeN IT caMe OuT foR tHE fIrst
tIme. but my meMory had fadEd fOr The lack of praCTiCe.

I re-ran thE coUrse again. I weNt oVer a coUpLe oF OnlinE ML Books Too.

TheN i startEd thinkinG oF The proBLem at haND. unFortUnately, it turned ouT
To BE a chicken and egG problEm. fOr THE feAtuRe to work peRfEctly WE needed A
LaRGE AmoUNt of training dAta to train oUR moDels. BuT wIthOUt the fEaturE
aCTuaLly deployed, We diDn't have anY way to COllect aNY trainiNg data.

so wE ulTimaTElY fEll BaCK to SImPle ALgo, That took It'S dEcisiOns bASED On a
fEw hArd cOded rUles. Things havE beEn woRKinG fine Till nOw.

~~~
heLlOgoodByeeee
they gAvE you two weeks to bEcomE a data sciEntIsT anD iMplemENt a worKinG
soLution? THat's nutS. i'm sTiLL PReTty early career, but I HaVE DoNe data
scIence wORk FoR About fouR yEarS now AND I wouLdve QUoTed at leaSt two mONtHs
To figurE oUt data, ClEan It, fEaturE EngiNeer, ruN models, cOmparE Results,
and then DEliver the best PErFORmINg soLutIon.

~~~
tedivm
AnD theY Didn'T eveN HaVe dATa!

~~~
pleTnes
NO dATa CLeaning required. That’S oFten 80% of A projEct. So 2 months -> 2
weEks makes seNSE Now!

------
fromtHestARt
mAchine Learning Is mucH more nuancED than peoPle seEm to undeRstand. You
CAN't just thRow DatA at A neT ANd Expect results-THIs fiElD requirEs a Heavy
deGreE of IntUitIoN, and enginEers must Be PrEpArEd fOr netS To pick up On
PatTeRns Not obVIous to HumanS, whiCh CaN LeaD to uninTUiTIVe reSuLtS.

NeuRal NeTs are BasiCAllY black bOx heuristIcs, WIth uNpReDictablE edge cases.
MucH lIke human reAsOniNg, I'd waRRAnt!

------
b_tterC_p
this doesn’t seem to offerS Any noveL peRspecTiveS. I read it aS inTended For
SElf marKeting.

~~~
E_ameiSeN
Co-AUThor hERE. THis PoSt came out oF A DisCussiOn With adam, wheRe we both
realized thAT thE advice we were GivIng tO ml tEamS AnD mL ENgINeeRS To guide
them to beTter resultS wERe Very often pROceSs Centric Rather tHan moDEl
centrIc.

Many
 me to go through the mult itudes ofMachi nelea ni ng M OOCs  out there and geta working prototype re ady i n2  wee k s. 

I h ad alre a dy d one  A nd rew  N g 's  cour seback wh en it came  ou tfor the firs t 
t ime. But m y m emory had fa ded  for t he  la c k ofpra ctice .

I r e -ran t he course  a g ain. I went ov e r  a  cou ple of online MLb oo k s too.

Then I  start ed th i n kin g o f the prob le m ath and. Un f or t u n ate l y, it turnedout
to be a chi cken a nd  eg g  probl em. F or the  feature to  wor k perfect ly wen eeded a large a mountoftra inin g  d at a t ot rai n  o urm ode ls. Butwi th out t he f e a t u re
a ctu ally  depl oyed, we didn 'thave any  wayto co llec tany t rainin g dat a. 

So we ultimat elyfell back  to  s imp le  algo ,  thattoo k i t's dec isionsb ase do n afew hard coded rul es . Th ings hav e been wo rk i ng fineti llnow. ~~~
h ello g oo dbyeeeeThey g a ve  y ou twowe eks to  b eco mea dat a  sci entist  and imp lemen t  a working
sol u tion? That'sn ut s .  I ' m st ill  pretty ear ly c a reer , bu t  I  ha v e don e dat as ci e nce work for aboutfo ur yea rs no w and I would ve quoteda t least tw o mo n th s 
to figure out  data , clean it ,  feature  engi neer, ru n models, c ompare r esu lts ,
a ndt h en  deliver theb e st p er forming  so lution. 

~ ~~ted ivm 
A nd they d idn't ev en  h av edat a!

~ ~ ~
pletne sNoda taclean i ng req uired . Th at ’s oft en 80%o fa proj ec t.  So2 m on ths - > 2w eek s makes  sense no w!---- -- 
f romthes tart
MachineLe arning ismu chmore n u a nced tha n people seem to unders t and. Yo u
c an'tjust  throw da taat a n eta nd exp ect re sults -t his f ieldr e qu ire sa heavy
de g r eeof in tu itio n, andengineers must bep rep ared for nets  to  pic k up  on pa t ter ns not o bvious to human s, which c an le ad to unin t uit iv e resul ts.

Ne uraln ets are  basicall y black b ox  heu r i sti cs, with u npredict able edge cases . 
M uch l ike  human  r eas oning , I'dw arr ant!
----- -b_tter c_p
t hi s doesn’ t seem to offers any no vel pe rspec tiv es. I rea d i t  as  intended  for 
self mark eti ng. 

~~~ 
e_ a meise n
Co - au tho r  here . Th i s post ca m eout o fa discussio n w ith  Adam , w h ere we  bot h
realized that t h e advice we we re g i v ing  to ML t ea ms and ML Engi nee rs t o guideth em to bette r result sw ere v ery of ten pr ocess c e n tr i c rath er t han  model
ce ntr ic . 

Many
 me_to go_through the multitudes of_Machine
leaning MOOCs_out_there and_get_a working prototype_ready in 2_weeks.

I had already done_Andrew Ng's course_back_when it came out for the first
time. But my memory had faded for the_lack_of practice.

I_re-ran_the_course again. I went over_a couple of online ML_books too.

Then_I started thinking of the problem at hand._Unfortunately,_it turned out
to_be a chicken and egg problem. For the feature_to work perfectly we needed a
large_amount of training_data_to_train our models. But_without the feature
actually deployed, we didn't_have any way to collect any_training data.

So we ultimately fell back to_simple algo, that took it's decisions_based on a
few hard coded_rules. Things_have been working fine till_now.

~~~
hellogoodbyeeee
They gave you_two weeks_to become a_data scientist and implement a working
solution?_That's nuts. I'm_still pretty early career, but I_have_done data
science work_for_about_four years_now and I_wouldve_quoted at_least_two months
to figure out data, clean_it,_feature engineer, run models, compare results,
and then_deliver the best performing_solution.

~~~
tedivm
And_they didn't even have_data!

~~~
pletnes
No data cleaning required. That’s_often 80% of a project. So_2 months_-> 2
weeks_makes sense now!

------
fromthestart
Machine Learning is much more nuanced than people seem_to understand. You
can't just throw data_at a net and_expect results-this_field_requires a heavy
degree_of_intuition, and_engineers must be prepared for nets to_pick up_on
patterns not obvious to humans, which_can lead to unintuitive_results.

Neural_nets are basically black box heuristics,_with unpredictable edge cases.
Much like human_reasoning, I'd warrant!

------
b_tterc_p
this doesn’t seem_to_offers_any novel perspectives. I read_it as intended for
self marketing.

~~~
e_ameisen
Co-author here._This post came_out of a discussion with Adam, where_we_both
realized that the advice we were_giving_to ML teams and ML Engineers_to_guide
them_to better results were very_often process centric rather than model
centric.

Many