If you're wondering which of the growing suite of programming language libraries and tools are a good choice for implementing machine-learning models then help is at hand.
More than 1,300 people mainly working in the tech, finance, and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey
The list is a mix of software frameworks and libraries for data science favorite Python, big data platforms, and cloud-based services that handle each stage of the machine-learning pipeline.
Most 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'.
Unsurprisingly, the most common form of ML being used as supervised learning, where a machine-learning model is trained using large amounts of labeled data. For instance, a computer-vision model tasked with spotting people in the video might be trained on images annotated to indicate whether they contain a person.
Here are the libraries, frameworks, big data platforms, and cloud services that businesses say they're using for machine learning.
Software libraries and frameworks
Google's widely used machine-learning framework
, designed to handle the numerical computation demanded when training machine learning models and able to split calculations between CPUs, GPUs and specialized chips such as Google's Tensor Processing Units (TPUs).
An open-source, deep learning framework
that has a reputation for being easier to learn than some competing frameworks like TensorFlow and that is designed to be used at each stage of the machine-learning pipeline.
Written in Python, it is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit (CNTK), and the Python library Theano.
Microsoft Azure ML Studio
This suite of services
is designed to help firms build, train, and deploy machine-learning models, both on Microsoft's Azure cloud and also on computing devices close to the edge of the network. Tools help automate the process of identifying and tuning an appropriate machine-learning model, as well as with scaling the underlying computer to match demand.
Google Cloud ML Engine
Similar to Azure ML Studio, Google Cloud ML Engine
also provides tools for training, evaluating, tuning, and deploying machine-learning models.
similarly offers services for building, training, and deploying machine-learning models, with a view to making it possible to get models to production more rapidly and at a lower cost.
Big data platform tools
The platform is designed to support the most widely used statistical and machine-learning algorithms and also offers a degree of automation to help data scientists identify and tune appropriate machine-learning models.
Designed to streamline the process of training and evaluating machine-learning models, Prodigy is a tool for helping data scientists annotate training datasets appropriately.
provides a Natural Language Processing (NLP) library designed to work with distributed systems running the in-memory, big-data platform Apache Spark.
The platform integrates Spark, TensorFlow, Keras, and the deep learning library BigDL, and can scale machine-learning models across distributed Hadoop and Spark clusters for training and inference.
Designed to simplify the process of designing and evaluating new deep-learning models for Natural Language Processing problems.
includes reference implementations of high-quality models for both core NLP problems and NLP applications.
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