Python Popularity Surging Because AI/ML Engineers Need It
The Python programming language has been topping virtually every tech trend list for the past two years, so it was no surprise to see it earn another “most popular” ranking in O’Reilly’s annual analysis of the most-used topics and the top search terms from its online learning platform. But the reason for Python’s latest blue ribbon is worth noting: according to O’Reilly, it was demand among data scientists and artificial intelligence (AI) and machine learning (ML) engineers.
Python is the go-to language for AI, ML and natural language programming (NLP) development, thanks in no small part to the dozen or so libraries and development tools that support it, from TensorFlow to Pytorch. And simple syntax and readability promote rapid testing of complex algorithms, and make the language accessible to non-programmers.
The O’Reilly analysis of its own data, published this week, found that Python accounted for 10 percent of all usage because of growing demand for AI/ML skills.
“Python has acquired new relevance amid strong interest in AI and ML,” the report states. “Along with R, Python is one of the most-used languages for data analysis. From pre-built libraries for linear or logistic regressions, decision trees, naïve Bayes, k-means, gradient-boosting, etc., there’s a Python library for virtually anything a developer or data scientist might need to do. (Python libraries are no less useful for manipulating or engineering data, too.)”
Usage specific to Python grew by just 4 percent in 2019, the analysts found, but usage that had to do with Python and ML — whether for AI, deep learning, or NLP, or in combination with any of popular ML/AI frameworks — grew by 9 percent.”
And yet, the analysts also noted that AI/ML “passions have cooled.”
“Up until 2017, the ML+AI topic had been among the fastest growing topics on the platform,” he report states. “Growth is still strong for such a large topic, but usage slowed in 2018 (+13 percent) and cooled significantly in 2019, growing by just 7 percent. Within the data topic, however, ML+AI has gone from 22 percent of all usage to 26 percent.”
So interest might be slowing while growing. The analysts also noted that data engineering as a practice area is being subsumed by both data science and AI/ML: “We know from other research that data scientists, ML and AI engineers, etc., spend an outsized proportion of their time discovering, preparing, and engineering data for their work. We’ve seen that popular tools and frameworks usually incorporate data engineering capabilities, either in the form of automated/guided self-service features or (in the case of Jupyter and other notebooks) an ability to build and orchestrate data engineering pipelines that invoke Python, R (via Python), etc., libraries to run data engineering jobs concurrently or, if possible, in parallel.”
There’s lots more in this study, which is based on “non-personally-identifiable” information about the top search terms and most-used topics on O’Reilly’s platform. Definitely a must read.
Posted by John K. Waters on 02/20/2020 at 11:38 AM