Pervasive Python | Technology Radar

Python is a language that keeps popping up in interesting places. Its ease of use as a general programming language, combined with its strong foundation in mathematical and scientific computing has historically led to its grassroots adoption by the academic and research communities. More recently, industry trends around AI commoditization and applications, combined with the maturity of Python 3, have helped bring new communities into the Python fold.

This edition of the Radar features a few Python libraries that have helped boost the ecosystem, including Scikit-learn in the machine learning domain; TensorFlow, Keras, and Airflow for smart data flow graphs; and spaCy which implements natural language processing to help empower conversationally aware APIs. Increasingly, we see Python bridging the gap between the scientists and engineers within organizations, loosening past prejudice against their favorite tools.

Architectural approaches such as microservices and containers have eased the execution of Python in production environments. Engineers can now deploy and integrate specialized Python code created by scientists through language- and technology-agnostic APIs. This fluidity is a great step toward a consistent ecosystem between researchers and engineers, in contrast to the de facto practice of translating specialized languages such as R to the production environments.

Read more on the Technology Radar

Olivia
over 4 years agoMarch 28, 2017
02:52:04 
Around 2:54, there's a subtitle issue. Where it's written "language diagnostic" the person says "language agnostic"
Bob
over 4 years agoMay 16, 2017 
Interpretive languages are great for one person or small group (3 or less) exploratory programming, but they are not usually a good fit for a mission critical deployment.

The trouble with interpretive languages in production environments is that too many errors get found at execution time.  The dynamic typing that makes them so fluid for exploratory work prevents a lot of the compile-time error detection that is available in a strongly types environement.  This lesson was learned back in the 1980's when AI work was usually done in lisp-like languages.  Too many times, after production deployment, a situation would occur that just poped up a debugger screen in the user's face.  Try that when flying an airplane, or running a medical life support device, or controlling a potentially explosive industrial process.

This is the reason James Gosling and the team at Sun decided to make Java a bridge between these 2 approaches.  Java is strongly types, and gets compiled, but runs in a portable virtual machine environment, and thus shares the benefits of both interpretive an compiled approached.  This is one of the main reasons for its great popularity.
Your comment