Worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, have been estimated to total $156.5 billion in 2020. The numbers are forecasted to surpass $300 billion in 2024 with a five-year compound annual growth rate (CAGR) of 17.1%, as per an IDC report.
With AI applications at the forefront of digital transformation initiatives across industries, it will be worthwhile to look at some of the top AI tools for 2021. The tools are bound to go a long way in easing the task of developers, much the same way as AI is easing businesses and our lives.
One of the popular AI tools, TensorFlow is an end-to-end open-source machine-learning platform. It has a comprehensive ecosystem of tools, libraries and community resources that help developers build and deploy ML-powered applications. With its simple yet flexible architecture, it allows a smooth flow from concept to code.
TensorFlow was originally developed by researchers working on the Google Brain team within Google’s Machine Intelligence Research organization. It provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages, which explains its widespread use.
Keras is a high-level neural network API which makes it extremely easy to prototype deep learning models. It is written in Python and supports multiple back-end neural network computation engines. Besides the ease of model building, Keras has a number of other advantages such as:
Support for a wide range of production deployment options
Integration with at least five back-end engines
Strong support for multiple GPUs
If you are a Python programmer and want a robust library that you can use to bring machine learning into a production system, you simply have to opt for Scikit-Learn. The library is built upon SciPy, which needs to be installed before you can use Scikit-Learn. The library is focused on modeling data. Some of the models provided by Scikit-Learn include:
An open-source deep learning framework, Mxnet is highly suited for flexible research prototyping and production. The big advantage with Mxnet is that it is highly scalable and supports multiple programming languages. It allows you to mix symbolic and imperative programming that enhances efficiency and productivity.
A Python library that allows you to optimize and evaluate mathematical expressions, Theano is built on top of NumPy. Some of the features of Theano that add to its popularity, include:
A deep learning framework, Caffe is known for its speed and modularity. Some of the characteristics of Caffe that endear it to developers include:
Its expressive architecture which is conducive to innovation
Its extensible code that fosters active development
It is known for its speed
It powers research projects and industrial applications, with equal ease.
An open source machine learning library, it is extensively used for applications such as natural language processing. It is known to accelerate the path right from research prototyping to production deployment. Some of its key features include:
#8 Google ML Kit
With the ML kit that is designed for mobile app development, it offers machine-learning expertise to mobile developers. It goes a long way in making iOS as well as Android apps more engaging. The ML Kit has the following:
H2O is an open-source, distributed machine learning platform that is known for its linear scalability. It supports statistical and machine learning algorithms and also has AutoML functionality.
The list of Artificial Intelligence Tools will not be complete without a mention of The Microsoft Cognitive Toolkit (CNTK). It is an open-source toolkit for commercial-grade distributed deep learning. With CNTK, users can combine model types such as:
Feed Forward DNNs
With these feature packed tools and frameworks, here’s to more innovation and widespread positive change!