Under various open-source licences, open-source AI is artificial intelligence technology that is accessible to the public for commercial and non-commercial use. Open-source AI makes it easier to create AI applications by providing datasets, prebuilt algorithms, and ready-to-use interfaces.
However, in recent years, AI has become much more available to the IT industry thanks to tools and frameworks. Artificial intelligence (AI)-based technologies are as a result quickly changing almost every area of our lives.
Six promising open-source AI frameworks and technologies are listed below for 2022.
TensorFlow
Many well-known companies use Google’s open-source framework TensorFlow, which is made up of a number of tools, libraries, and resources:
among others, Dropbox, eBay, and Airbnb.
To speed up development, TensorFlow abstracts and simplifies machine learning algorithms. As a result, with the use of visual models and flowgraphs, developers and data scientists can easily use data to design neural networks and other machine learning models. For instance, Airbnb utilises TensorFlow to categorise images of rental apartments to make sure they appropriately depict the space.
Theano
Theano is a deep learning package for Python that integrates well with NumPy. Its main goal is to define and analyse complex mathematical statements using relatively straightforward Python scripts while utilising high-performance computers. Although many businesses use frameworks developed on top of Theano, such as Keras or Blocks, it is still thought of as a low-level framework.
SageMaker Neo by Amazon
Amazon just made Amazon SageMaker Neo, a part of their machine learning technology, available as a service. Machine learning models can now be trained and used anywhere in the cloud thanks to the Neo-AI project’s freshly released code. Neo-AI is additionally optimised for Internet of Things (IoT) sensors and edge computing devices, which need low latency and speedy predictions.
Cognitive Toolkit for Microsoft
A platform for deep learning that is open-source is Microsoft Cognitive Toolkit (CNTK). CNTK can be used as a standalone machine learning tool via its model description language, BrainScript, or it can be incorporated into projects as a library in a variety of computer languages. The commercial-grade toolkit is used by Skype, Bing, Cortana, and other businesses with huge datasets that require a scalable and highly effective machine learning platform.
Scikit-learn
The open-source Python machine learning library Scikit-learn focuses on data mining and analysis. It is based on NumPy, SciPy, and matplotlib and includes a selection of excellent ML models for the most typical uses. Well-known businesses like Spotify, J.P. Morgan, and Evernote use Scikit-learn to do data-driven activities including predictive analysis, individualised recommendations, and more.
Keras
High-level machine learning APIs like TensorFlow, Microsoft Cognitive Toolkit, and Theano are compatible with Keras. Keras is the tool for quickly creating new applications because of its user-friendliness and focus on the developer experience. Many businesses, like Netflix, Uber, and Yelp, as well as smaller startups, have used Keras into their core goods and services. Netflix, a subscription-based company, uses deep learning to predict customer churn, which is important. The business has access to a sizable amount of consumer data. It can recognise customers who are likely to stop using their service and entice them to stay by giving them discounts and other benefits.
