Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize the way we live and work. AI software development tools are becoming increasingly popular, as they offer a range of benefits, including automating tasks, enhancing user interaction, and improving business operations.
In this article, we will discuss the top 10 best AI software development tools to consider in 2023.
Top 10 Best AI Software Development Tools 2023
1. Tabnine
Tabnine is an AI-powered code completion tool that uses machine learning to predict the next line of code you are likely to write. It supports a range of programming languages, including Python, JavaScript, Java, and C++. Tabnine can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and PyCharm.
2. TensorFlow
TensorFlow is an open-source software library for machine learning and artificial intelligence. It was developed by the Google Brain team for internal Google use in research and production and was released under the Apache License 2.0 in 2015. TensorFlow can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
3. Codota
Codota is an AI-powered code completion tool that uses machine learning to suggest code snippets and functions as you type. It supports a range of programming languages, including Python, JavaScript, Java, and C++. Codota can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and Eclipse.
4. Viso Suite Platform
Viso Suite is an end-to-end computer vision application platform that provides the software infrastructure to develop, deploy, scale, and secure AI vision applications. It is used by a range of large companies to deliver and maintain their portfolio of computer vision applications.
5. IBM Watson
IBM Watson is a suite of pre-built applications and tools to build, run, and manage AI. It offers a range of capabilities, including natural language processing, machine learning, and computer vision. IBM Watson can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and Eclipse.
6. Google Cloud Learning Machine
Google Cloud Learning Machine is a cloud-based platform that provides access to Google’s cutting-edge AI technology, including Tensorflow, TPUs, and TFX tools. It offers a range of pre-built applications and tools to build, run, and manage AI. Google Cloud Learning Machine can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and Eclipse.
7. NVIDIA Deep Learning AI Software
NVIDIA Deep Learning AI Software is a suite of pre-built applications and tools to build, run, and manage AI. It offers a range of capabilities, including natural language processing, machine learning, and computer vision. NVIDIA Deep Learning AI Software can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and Eclipse.
8. Azure Machine Learning Studio
Azure Machine Learning Studio is a cloud-based platform that provides access to a range of pre-built applications and tools to build, run, and manage AI. It offers a range of capabilities, including natural language processing, machine learning, and computer vision. Azure Machine Learning Studio can be integrated into a range of development environments, including Visual Studio Code, IntelliJ IDEA, and Eclipse.
9. Snyk.io
Snyk.io is a developer-first platform for securing code, dependencies, containers, IaC, and cloud deployments. It is a developer security platform that enables application and cloud developers to secure their whole application, finding and fixing vulnerabilities from their first lines of code to their running cloud. Snyk tests for vulnerabilities in your own code, open source dependencies, container images, infrastructure as code configurations, and cloud environments and offers context, prioritization, and remediation.
10. Apache MXNet
Apache MXNet is an open-source deep learning software framework that is used to train and deploy deep neural networks. It is a flexible and efficient library for deep learning that is suited for flexible research prototyping and production. MXNet is scalable, allowing for fast model training, and supports a flexible programming model and multiple programming languages, including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language.
