So, How Many ML Models You Have NOT Built?
What a weird question. That’s what you would have thought after reading the headline. Perhaps you thought the word “NOT” was accidental.
Hmm, for past few years many of us have come across articles like
The list is endless. Any new Data Science aspirant already waves the white flag just by merely seeing the “SHOULD KNOW” type articles on the internet.
At the end of the day, a person does not know where to start in the first place due to the overwhelming amount of information.
What I have described above is a Problem from an aspiring Data Scientist point of view.
There is a bigger problem due to the “SHOULD KNOW” type of articles and the problem bearer are companies- both startups and big MNCs.
What are the problems you ask ?
Everybody wants to have a pie of the latest in thing “ Data Science” .
Many companies want to do Data Science and as things are new for many of these companies, the job description is often strange and the interview process even stranger.
Some of these companies influenced by “SHOULD KNOW” kind of articles tell the job applicant
The newly minted Data Scientists quickly blurt out 2–3 ML algorithms and the enamored company hires him/her . In due course of time the algorithms are implemented. The Data Scientist impresses the company with good accuracy % of the models. The models are put in production. But lo and behold, the model does not net the company the ROI it hoped for. What happened?
Well what happened was the Data Scientist did not have business acumen and thought his/her KPI was just building ‘good’ ML models. The company had business acumen but not the Machine Learning / Statistics Knowledge. The ideal marriage never happened.
We all have heard of this story or the variant of the story.
A ship company hired an Engineer to fix the engine of the ship. The Engineer had all the tools in his toolkit. After some analysis the engineer took out a hammer and hit one of the components of the engine. The Engine started to work.Next day the Engineer sent the invoice to the Ship company for a whopping $10,000 for hardly a 5 min job.
The Ship company manager was taken aback and asked the Engineer to itemize the invoice. The bill read as follows
Now you may think I am laying emphasis on Domain Knowledge and Experience, yes you guessed it right.
The Engineer in the story had all the tools in his toolkit, yet chose only the hammer (perhaps the simplest tool) to fix the engine. Also, most importantly he knew where the problem was .
