Why do we have to train a model from scratch every time? I have started on Andrew Ng's machine learning course. It seems that machine learning is learning correlations with known data based on as many parameters as possible. For example, if we collect data on existing property prices with information on the land area, built-in area, type of building, age of the building, etc, it is possible to predict the price of another property if we input the value of the various parameters of this property.
Similarly, if we keep the images (the black and white pixels) of cats, we can tell whether a new picture is a cat if it bears some resemblance to the pixels of existing labeled cat images.

This approach sounds great, but is it practical? How much effort and zettabytes of data do we have to keep just to reach the brainpower of, say, a 3-year old, who can recognize dogs, cats, tigers, a Mustang, trucks, a hamburger restaurant, and so on?

Why does everyone have to repeat the effort of learning the same things?

If Google has already learned cats, or if someone already has a program to recognize handwritten digits, can this knowledge be shared and re-used? Or is it just a matter of paying for them?
Disclosure: I am a product manager on Google Cloud Platform.


  [...] why does everyone have to repeat the effort of learning the same things?
  
  If Google has already learned cats, or if someone already has a program to recognize handwritten digits, can this knowledge be shared and re-used? Or is it just a matter of paying for them?


You don't have to rebuild these machine learning models from scratch; you can reuse prebuilt machine learning algorithms, e.g., Google Cloud provides the following hosted APIs as a service:


Google Cloud Vision API to recognize images and provide text labels
Google Cloud Speech API to convert speech to text
Google Cloud Natural Language API to understand language, find entities, etc.
and more...


You can put these APIs together to build interesting applications, e.g.,


build a smart Raspberry Pi bot with Cloud Vision and Speech APIs (video presentation; blog post + video overview)