Here’s how it’s dying at Google. :)
In the beginning, the founders of Google made the decision
of “Python where we can, C++ where we must.” This meant that C++ was used where memory control was imperative and low latency was desired. In the other facets, Python enabled for ease of maintenance and relatively fast delivery.
Even when other scripts were written for Google in Perl or Bash, these were often recoded into Python. The reason was because of the ease of deployment and how simple Python is to maintain. In fact, according to Steven Levy
– author of “In the Plex,” Google’s very first web-crawling spider was first written in Java 1.0 and was so difficult that they rewrote it into Python.
Python is now one of the official Google server-side languages—C++, Java, and Go are the other three—that are allowed to be deployed to production. Many web hosts use it as well.
To top it all off, Peter Norvig said:
“Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we’re looking for more people with skills in this language.”
Here’s how it’s dying on Quora.
According to Adam D’Angelo, they decided not to go with C# because it’s a proprietary Microsoft language and they didn’t want to be beholden to any future changes put out. Additionally, any open source code had second-class support at best.
Java was more painful to write in than Python and it didn’t play as nicely with non-Java programs as Python did. At the time, Java was also in its infancy, so they were worried about future support and if the language would continue to grow.
Instead, the founders of Quora took their lead from Google, choosing to use Python where they could because of its ease of writing and readability, and implemented C++ for the performance critical sections. They got around Python’s lack of typechecking by writing unit tests that accomplish much the same thing.
Another key consideration for using Python was the existence of several good frameworks at the time including Django and Pylons. Additionally, because they knew that Quora was going to involve server/client interactions that wouldn’t necessarily be full page loads, having Python and JS play so well together was a huge plus.
Here’s how it’s dying at Netflix.
Netflix uses Python in a very similar manner to Spotify, relying on the language to power its data analysis on the server side
. It doesn’t just stop there, however. Netflix allows their software engineers to choose what language to code in, and have noticed a large upsurge in the number of Python applications.
When surveyed, Netflix engineers cite the standard library, the extremely active development community, and the rich variety of third party libraries available to solve nearly any given problem. Additionally, because Python is so easy to develop, it has become a linchpin in many of Netflix’s other services.
Here’s how it’s dying at Reddit.
This website had 542 million visitors every month across 2017, making it the fourth most visited website in the United States
and seventh most visited in the world. In 2015, there were 73.15 million submissions and 82.54 billion pageviews. And behind it all, forming the software backbone, was Python.
Reddit was originally coded in Lisp, but in December of 2005, six months after its launch, the site was recoded into Python. The primary reason for the change was that Python had a wider range of code libraries and was more developmentally flexible.I would definitely say so. In my last 3 jobs as both a data science manager and data scientist, I’ve tried to use machine learning but in each job it is the same story.
The company states in the job description they want somebody with ML knowledge and the know-how to apply ML to solve current problems. Once you get hired, it is a completely different story.
- You find out you were hired to do just data analysis in excel or R or Python because no ML currently exists.
- You find out data is sparse and that the company actually doesn’t have the right features to do what it wants. Ultimately, real-world problems show a high degree of non-linearity and the easy canned ML solutions that you learned in college don’t really apply. A typical real-world scenario is, you run a model and the model comes back with a 45% accuracy, which is not much better than a 25% random guess. At this point, flipping a coin would be better than machine learning.. lol
- You find out you are a SQL junkie and you write SQL all day long to retrieve data. 90% of the hype is around getting the data right and building dashboards. lol
- You find out you have a manager who is apprehensive about ML because he feels it is a black box and he doesn’t know what is going on inside it. So he is not about to let ML take over even as an experiment because he feels ML is unpredictable and that the business may lose money. So your dreams of putting ML models into production are a pipe dream far in the distant future.
- You find out IT is not cooperative and that they see you as the hotshot new kid in town with skills that nobody understands. They are afraid that if they let you take over it will diminish their power. Some simply don’t understand and they feel you might take over their jobs. For example, ML has largely moved to the cloud now but in order to access those cloud resources and serverless architecture to implement ML in production, you need full rights. IT will simply deny those rights to you because of the aforementioned reasons. The manager doesn’t want to upset the current working conditions, so he won’t know what to do because the bottom line is to keep the current business running and the priority is not to have you try fancy ML techniques that are unproven which may or may not bring a profit.
Ultimately, you are lured into the job with aspirations of putting big ML models into production and doing cutting edge algorithm research only to find out that the company is not ML ready and that they only hired you so they can say to their clients, “Yes, we have a machine learning guru onboard”