Software application is one of the most routine activities spread across organizations and its use has only increased over the years. Software development is a complex and difficult task, with the process requiring ideation, product definition, coding, strategic design, quality assessment and more. If mistakes are made during these steps, it may mean restarting the entire process.
Because of these challenges, many of today’s best organizations are turning to artificial intelligence (AI) and machine learning (ML) to achieve better results.
Here are the 7 best tools for developing AI software:
1. Google Cloud AI Platform
Topping our list of the 7 best tools for AI software development is the Google Cloud AI Platform, which allows you to 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 platform’s integrated toolchain accelerates machine learning project development and deployment, and you can build portable ML pipelines with Kubeflow, an open source platform from Google. The AI platform allows you to deploy your machine learning project both on-premise and in the cloud, and there are various options for storing data, such as cloud storage and BigQuery.
Another key offering 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. . In addition, 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 key features of the Google Cloud AI Platform:
- Helps train, analyze and tune your ML models
- Get predictions, monitor predictions and manage your models
- Accelerates ML projects
- ML project on premise or in the cloud
- Preconfigured Virtual Machines (VMs)
2. Microsoft Azure AI platform
Another top option for AI software development tools is the Microsoft Azure AI Platform, which is popular in the development market. A robust cloud platform, many organizations rely on it. The platform has many great capabilities such as object recognition, unattended machine learning, speech recognition and more.
The Microsoft Azure AI Platform has made it easier for organizations to conduct AI development and offers various customization options, such as domain-specific pre-trained AI models for text, face, and speech recognition. It also offers several machine learning capabilities, such as 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 key 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
3. IBM Watson
Another of the most well-known AI tools for software development is IBM Watson, which is equipped with advanced AI capabilities, including conversational interfaces, mobile app APIs, conversational interfaces, and NLP services.
IBM Watson’s robust software development tools have been deployed in many different industries, such as oil and gas, media and entertainment, financial services, Internet of Things (IoT), and many more.
The platform also offers AI development services that allow developers to build apps with pre-trained AI capabilities. Some of the other features include Watson Assistant, which integrates to build AI-powered conversational interfaces into your app, and the NLP service in the form of Watson Natural Language Understanding (NU).
Here are some of IBM Watson’s key features:
- Rapid development and deployment for AI applications
- Speech-to-text capabilities
- NLP and NLU services
- Powerful APIs for mobile apps
H20 is an open-source, machine learning platform that is steadily growing in popularity. It is a powerful AI software development tool that offers a wide range of features such as 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 such as R and Python to build models, is commonly used for data analytics, fraud detection, risk management, digital healthcare, and customer intelligence.
Here are some of the key features of H20:
- Includes AutoML functionality
- Supports many algorithms such as gradient gain machines, deep learning, generalized linear models, etc.
- Linear scalable platform
- Developing mobile apps with R, Java and Python
No list of AI software development tools would be complete without TensorFlow, an open-source, numerical computation tool and machine learning library. Primarily used for development and research purposes, TensorFlow is flexible while providing 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 service, and more.
Here are some of the key features of TensorFlow:
- Seamlessly deploy AI-based solutions on mobile devices, desktops, edge devices, etc.
- Comprehensive set of tools, libraries and community resources
- In-depth analytics, service automation, unsupervised training, etc.
- Wide range of features and functionalities
MxNet is a leading and popular AI software development tool that is an in-depth learning framework commonly used in research and production. MxNet helps improve AI development with advanced natural language processing capabilities and provides distributed training models to scale AI applications.
The tool is commonly used in Multi-GPU and multi-machine training, as well as when writing custom layers in high-level languages.
Here are some of the key features of MxNet:
- Open-source framework for deep learning
- Enhances AI-based applications with NLP
- Highly scalable framework
- Defining, training and implementing neural networks
7. Infosys Nia
We conclude our list of the 7 best AI software development tools with Infosys Nia, another great tool for building powerful AI-powered apps. One of the best features the tool offers is Nia Advanced ML, which offers a wide range of machine learning algorithms that work quickly and at scale and help build high-performing ML models.
The tool also allows you to build AI-powered chatbots, giving your app 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 key features of Infosys Nia:
- Effectively build ML training models
- Process large data sets at scale
- Data Management Capabilities
- Nia chatbot