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7 Best AI Software Development Tools

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Software application is one of the most routine activities spread across organizations, and its use has only increased over the years. The development of software is a complex and difficult task, with the process requiring ideation, product definition, coding, strategic designing, quality assessment, and more. If there are any mistakes made during these steps, it could mean the entire process needs to be restarted. 

Because of these challenges, many of today’s best organizations turn to artificial intelligence (AI) and machine learning (ML) to achieve better outcomes. 

Here are the 7 best AI software development tools: 

1. Google Cloud AI Platform

Topping our list of 7 best AI software development tools is the Google Cloud AI Platform, which helps you train your machine learning models at scale, host your trained model in the cloud, and use your model to make predictions about new data. 

The integrated toolchain provided by the platform expedites the development and deployment of machine learning projects, and you can build portable ML pipelines with Kubeflow, an open-source platform from Google. The AI platform enables you to deploy your machine learning project both on-premise or in the cloud, and there are various options to store data, such as cloud storage and BigQuery. 

Another one of the main offerings of AI Platform is pre-configured Virtual Machines (VMs), which help develop deep learning applications, and it also has natural language processing (NLP) capabilities that help determine the meaning and structure of text. Besides these, you can use the platform's APIs for speech-to-text and text-to-speech capabilities, as well as vision capabilities to extract insights from images.

Here are some of the main features of the Google Cloud AI Platform: 

  • Helps in training, analyzing, and tuning your ML models
  • Get predictions, monitor predictions, and manage your models
  • Expedites ML projects
  • ML project on-premise or in the cloud
  • Pre-configured Virtual Machines (VMs)

Visit Google Cloud →

2. Microsoft Azure AI Platform

Another top option for AI software development tools is the Microsoft Azure AI Platform, which has been trending in the development market. A robust cloud platform, there are many organizations that rely on it. The platform has many great capabilities, such as object recognition, unsupervised machine learning, speech recognition, and more. 

The Microsoft Azure AI Platform has made it easier for organizations to carry out AI development, and it offers various customization options like domain-specific pre-trained AI models for text, face, and speech recognition. It also offers various machine learning capabilities like Azure ML, a Python-based automated ML service; Azure Databricks, an Apache Spark-based big data service; and ONNX, an open-source model format and runtime for ML.

Here are some of the main features of the Microsoft Azure AI Platform:

  • Python-based automated ML service
  • Machine translation
  • Object, speech, and text recognition capabilities
  • AI templates for creating bots

Visit Microsoft Azure →

3. IBM Watson

Another one of the most well-known AI tools for software development is IBM Watson, which is equipped with advanced AI capabilities including conversation interfaces, mobile app APIs, conversation interfaces, and NLP services. 

IBM Watson’s robust software development tools have been deployed throughout many different industries like oil and gas, media and entertainment, financial services, Internet of Things (IoT), and much more. 

The platform also offers AI development services that help developers build apps with pre-trained AI capabilities. Some of its other features include Watson Assistant, which can be integrated to build AI-powered conversational interfaces into your app, and its NLP service in the form of Watson Natural Language Understanding (NLU). 

Here are some of the main features of IBM Watson: 

  • Quick development and deployment for AI applications
  • Speech-to-text capabilities
  • NLP and NLU services
  • Powerful mobile app APIs

Visit IBM Watson →

4. H20

H20 is an open-source, machine learning platform that has been steadily rising in popularity. It is a powerful AI software development tool that offers a wide range of features like smart machine learning algorithms, statistical and generalized linear models, deep learning models, and more. The tool also supports big data, Hadoop, and Spark/Kubernetes clusters. 

H20, which allows you to use programming languages like R and Python to build models, is often used for data analysis, fraud detection, risk management, digital healthcare, and customer intelligence. 

Here are some of the main features of H20: 

  • Includes AutoML functionality
  • Supports many algorithms like gradient boosted machines, deep learning, generalized linear models, etc. 
  • Linearly scalable platform
  • Develop mobile apps using R, Java, and Python

Visit H2O →

5. TensorFlow

No list for AI software development tools would be complete without TensorFlow, which is an open-source, numeric computational tool and machine learning library. TensorFlow is primarily used for development and research purposes, and it is flexible while offering comprehensive tools and libraries that help accelerate AI development. 

Businesses can use TensorFlow to develop and train smart ML and AI-powered applications that handle complex operations including forecasting, contract management, legal operations, customer services, and more. 

Here are some of the main features of TensorFlow: 

  • Seamlessly deploy AI-based solutions on mobiles, desktop, edge devices, etc. 
  • Extensive set of tools, libraries, and community resources
  • Deep analytics, service automation, unsupervised training, etc. 
  • Wide range of features and functionalities 

Visit TensorFlow →

6. MxNet

MxNet is a leading and popular AI software development tool that is a deep learning framework often used in research and production. MxNet helps enhance AI development with advanced natural language processing capabilities, and it offers distributed training models to scale AI applications. 

The tool is often used in Multi-GPU and multi-machine training, as well as in writing custom layers in high-level languages. 

Here are some of the main features of MxNet:

  • Open-source deep learning framework
  • Enhances AI-based applications with NLP
  • Highly scalable framework
  • Define, train, and deploy neural networks

Visit MxNet →

7. Edgeverve

Closing out our list of 7 best AI software development tools is Xtractedge by Edgeverve, which is another great tool that enables you to build powerful AI-powered apps. One of the best capabilities offered by the tool is Nia Advanced ML, which offers a wide range of machine learning algorithms that operate at speed and scale, helping build high-performing ML models. 

The tool also enables you to build AI-powered chatbots, with your app providing access to the enterprise knowledge repository. The app can also automate actions with a conversational interface and integrate Nia data, which is a robust analytics solution. 

Here are some of the main features of Infosys Nia: 

  • Effectively build ML training models
  • Process large data sets at scale
  • Data management capabilities 
  • Nia chatbot

Visit Edgeverve →


In conclusion, artificial intelligence (AI) and machine learning (ML) are revolutionizing the software development landscape by enhancing the efficiency, accuracy, and capabilities of development tools. These advanced technologies streamline complex processes such as ideation, coding, and quality assessment, significantly reducing the potential for errors and increasing overall productivity.

AI-powered tools offer a range of functionalities, from natural language processing (NLP) and deep learning to automated machine learning (AutoML) and pre-trained models, catering to various industry needs. They facilitate the development of robust applications, support data analysis, improve decision-making, and enable the creation of intelligent, scalable solutions.

As organizations increasingly adopt AI and ML tools, they gain a competitive edge through improved efficiency, reduced development time, and enhanced software capabilities. The tools highlighted in this article represent some of the best available, each offering unique features and benefits that cater to diverse development needs. Embracing these technologies will undoubtedly continue to drive innovation and excellence in software development.

Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.