As is true in all things software development, the execution is just as important as the concept; and when it comes to machine learning and AI, the language you use to build the models and algorithms are just as important as the use-case concept you’ve devised. To that end, a new O’Reilly study handicaps the most prevalent languages available today to give us a snapshot of the preferred programming languages across the AI development spectrum. It shouldn’t come as a huge surprise that Google’s TensorFlow tops the list, but it’s worth breaking down the leaders and why they are indeed leading.
According to the report as detailed in TechRepublic, “[m]ost firms are still at the evaluation stage when it comes to using machine learning, or AI as the report refers to it, and the most common tools being implemented were those for ‘model visualization’ and ‘automated model search and hyperparameter tuning’.”
To that end, here are all the languages or frameworks getting at least double digit usage percentages in the survey:
TensorFlow: Hailing from Google, TensorFlow is a widely used machine-learning framework designed to “handle the numerical computation demanded when training machine learning models”. It also enables those systems to split calculations between CPUs, GPUs and specialized chips such as Google’s Tensor Processing Units (TPUs).
Scikit-learn: Self-described as ‘machine learning in python,’ this popular Python library for data mining and data analysis implements a wide-range of machine-learning algorithms.
Keras: Pairing with brain-inspired neural networks, Keras is a deep-learning framework designed to be a simpler working alternative than competing frameworks for similar work (i.e. other deep-learning languages). “Written in Python, it is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit (CNTK), and the Python library Theano”, according to TechRepublic.
Pytorch: Also a machine-learning framework, this one is designed to be even easier than TensorFlow (starting to notice a pattern here? A lot of these languages are obsessed with simplifying otherwise staggeringly complex processes).
“An open-source, deep learning framework that has a reputation for being easier to learn … and is designed to be used at each stage of the machine-learning pipeline.”
Azure ML studio, Google Cloud ML Engine and Amazon Sagemaker: All three of these machine learning cloud suites are designed as end-to-end development and deployment tools. They’re each ‘designed to help firms build, train, and deploy machine-learning models’, with slight variations in each (to deep dive that, go here).
Spark NLP: Simply enough, Spark NLP “provides a Natural Language Processing (NLP) library designed to work with distributed systems running the in-memory, big-data platform Apache Spark,” according to TechRepublic.
For now, these are the most popular libraries, frameworks and development suites out there. Understanding what developers are using, as well as why and how they’re using it, can be crucial to the current and future success of any AI-inspired upgrades to your business or its technology stack. So pay attention to these to stay ahead of the curve!
Jeff Francis is a veteran entrepreneur and co-founder of Dallas-based digital product studio ENO8. Jeff and his business partner, Rishi Khanna, created ENO8 to empower companies of all sizes to design, develop and deliver innovative, impactful digital products. With more than 18 years working with early-stage startups, Jeff has a passion for creating and growing new businesses from the ground up, and has honed a unique ability to assist companies with aligning their technology product initiatives with real business outcomes.
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