The software application is one of the most common activities in organisations, and its use has only grown over time. Developing software is challenging. If any errors happen during the process, the entire process may need to restart.
Numerous AI-enabled software development tools will allow important tasks to be completed accurately and efficiently. Many organisations are incorporating machine learning and artificial intelligence to achieve great results. The overall marketing trends are changing due to software development.
As we all know, software development is a complex process requiring product, creativity, strategic coding, or designing. Numerous AI software tools available have already transformed a programmer’s overall mindset.
As a result of these challenges, many of today’s most successful organisations are turning to AI and ML (Machine Learning) to achieve better results.
The following are the top seven AI software development tools:
1. Google Cloud AI Platform
Google’s Cloud AI Platform ranks first on our list because it allows you to train machine learning models at scale, put your trained model in the cloud, and utilise your model to predict new data.
The platform’s integrated toolchain speeds up the development and deployment of machine learning projects, and you can build portable ML pipelines with Kubeflow, a Google open-source platform. The AI platform allows you to deploy your machine learning project on-premises or in the cloud, and there are several data storage options, including cloud storage and BigQuery.
Another of AI Platform’s main offerings is pre-configured Virtual Machines (VMs), which aid in developing deep learning applications and natural language processing (NLP) capabilities, which help determine the meaning and structure of the text. Aside from these, the platform’s APIs can be used for speech-to-text and text-to-speech capabilities and vision capabilities for extracting insights from images.
Below are some of the prime features of the Google Cloud AI Platform:
- Aids in the training, analysis, and tuning of machine learning models.
- Receive predictions, track predictions, and manage your models
- Accelerates machine learning projects
- On-premises or cloud-based machine learning projects
- Pre-configured virtual Machines (VMs)
2. Microsoft Azure Artificial Intelligence Platform
Another top alternative for AI software development tools is the Microsoft Azure AI Platform, which has been trending in the development market. The platform has many useful features, including object recognition, unsupervised machine learning, speech recognition, and others. Many organisations rely on it as a strong cloud platform.
The Microsoft Azure AI Platform has made AI development convenient for organisations. It provides domain-specific pre-trained models for text, face, and speech recognition. Azure ML is a Python-based automated ML service. Azure Databricks is an Apache Spark-based large data service, ONNX is an open-source model format, and ML runtime is available.
Some of the key features of the Microsoft Azure AI Platform are as follows:
- Python-based machine learning service
- Translation by machine
- Capabilities for object, speech, and text recognition
- AI bot-creation templates
3. IBM Watson
IBM Watson is another well-known AI tool for software development with advanced AI capabilities. It has AI capabilities such as conversation interfaces and mobile apThe platform also provides AI development services, which enable developers to create apps with pre-trained AI capabilities. Other features include Watson Assistant, which IBM Watson helps to integrate into your app to develop AI-powered conversational interfaces, and its NLP service, Watson Natural Language Understanding (NLU).
IBM Watson’s powerful software development tools have been used in many industries, including oil and gas, media and entertainment, financial services, the Internet of Things (IoT), and many others.
A few of IBM Watson’s key features are as follows:
- AI applications can be developed and deployed quickly.
- Capabilities for speech-to-text
- Services for NLP and NLU
- Watson has APIs for powerful mobile apps, conversation interfaces, and NLP services.
4. H20
H20 is a growing open-source machine learning platform. It is a strong AI software development platform that includes various features such as intelligent machine learning algorithms, statistical and generalised linear models, deep learning models, and much more. The tool supports big data, Hadoop, and Spark/Kubernetes clusters.
H20, which allows you to create models in programming languages such as R and Python, is frequently used for data analysis, fraud detection, risk mitigation, digital healthcare, and customer insights.
Here are some of H20’s main characteristics:
- AutoML functionality is included.
- Many algorithms are supported, including gradient-boosted machines, deep learning, generalised linear models, and others.
- Platform with linear scalability
- Create mobile apps with R, Java, and Python.
5. TensorFlow
TensorFlow, an open-source numerical computational tool and machine learning library, would be missing from any AI software development tools list. TensorFlow is principally used for development and research and is adaptable, offering complete tools and libraries that aid in accelerating AI development.
TensorFlow’s main features are as follows:
- You can easily deploy AI-based solutions on mobile, desktop, and edge devices using TensorFlow.
- A large number of tools, libraries, and community resources are available.
- Deep analytics, service automation, unsupervised training, and other technologies
- It offers countless features and functionalities that are very useful for users.
6. MxNet
MxNet is a well-known and widely used AI software development tool that is a deep learning framework frequently used in research and production. MxNet enhances AI development by providing advanced natural language processing capabilities and distributed training models for scaling AI applications.
The tool is frequently used in multi-GPU and multi-machine training and in developing custom layers in high-level languages.
MxNet’s key features are as follows:
- Deep learning framework open source
- NLP is used to improve AI-based applications.
- The extremely scalable framework
- Neural networks must be defined, trained, and deployed.
7. Infosys Nia
Infosys Nia, another great tool for building powerful AI-powered apps, rounds out our list of the seven best AI software development tools. One of the best capabilities supplied by the tool is Nia Advanced ML, which provides a wide selection of machine learning algorithms that function at speed and scale, assisting in building high-performing ML models.
You may also use the tool to construct AI-powered chatbots, providing your app access to the enterprise knowledge library. The app can also use a conversational interface to automate actions and integrate Nia data, a powerful analytics solution.
Here are some of Infosys Nia’s key features:
- Create ML training models that work.
- Scalable processing of large data sets
- Capabilities for data management
- Nia chatbot
So, why do you think AI-enabled software development tools are important? Let’s look at a few reasons that would support the statement. Read on.
Why Are AI-Enabled Software Development Tools important?
To fulfill the demands of the increasingly digitally connected client, software companies must react quickly to behavioural changes in order to develop and distribute products quickly and accurately.
Traditional software development is not built to enable these changes, as it entails a number of steps, such as manually writing code, preparing requirements, designing software, and testing to ensure that the final result fulfills standards. Artificial intelligence (AI) is changing this process by enabling the creation of scalable and efficient procedures that increase productivity while decreasing time-to-market. Though many software businesses are still in the early stages of AI application, the technology’s utilisation in the enterprise is gradually increasing. According to Tractica, global revenue from the use of AI tools is predicted to reach $119 billion by 2025.
Artificial intelligence algorithms and advanced analytics enable software development teams to make fast decisions based on real-time data at scale. As opposed to robots that respond to rules-based reasoning or offer pre-determined replies, AI applications execute sophisticated and intelligent operations associated with human thinking. AI algorithms can automate the coding process by integrating data from various sources, including microchips, sensors, and remote inputs, to assist developers in writing correct code, resulting in more efficient, agile, and scalable processes.
AI does not replace human intelligence; rather, it constantly learns and delivers human interactions, saving software development teams time by minimising human error associated with duplicate activities.
Let’s look at four specific ways that AI can help to streamline the software development process:
Agile Project Management
The advantages of using AI for efficiency go beyond normal task management. Developers, for example, spend a substantial amount of time manually handling code documents, which increases the chance of coding typos and other such issues. AI- and ML-enabled algorithms can assist software businesses in reducing documentation time by recognising and tracking past coding patterns. This computer-assisted technology can also increase real-time communication by guaranteeing that project team members from different departments and locations obtain correct coding and testing history.
Artificial intelligence in software development can assist developers in being more agile and lean in how code is produced, tested, and delivered to production teams. AI augments and streamlines software development procedures, alleviating frustration from the development or testing process. AI algorithms can be used to enhance project deadlines, cost estimates, and volumes by allowing development teams to prioritise code sections that need to be completed quickly and become more exact in describing likely failure rates.
AI/ML systems analyse bigger code databases proactively for anomalies, reporting potential future steps to developers for protection. This is useful not only for recovery but also for error pre-diagnosis. Missing code, errors, or alternate product or service names under the same code are examples of abnormalities.
Automate Software Development
Designing software code is an important, complex, and time-consuming aspect of the development process, especially for distributed teams. Planning and developing a project requires collaboration between developers, designers, R&D, and marketing teams through transparency and good communication. While all of this is done manually, AI/ML algorithms can help to streamline and automate the planning and designing process by gathering data such as project stakeholders names, location, customer needs, products, and business types to auto-create intuitive instructions on which design approach to take without requiring manual intervention. Automating the code design process can help programmers save time, effort, and money.
The Artificial Intelligence Design Assistant is an example of AI assisting in creating software design (AIDA). This website-building platform quickly evaluates data provided by a user to develop a web design according to the user’s preferences. AIDA automatically proposes and applies code for each website’s best design components, colours, and layouts.
Streamlining Software Testing
Every software delivery cycle includes testing. Current software testing techniques are costly, inefficient, and time-consuming because many flaws are discovered after the product has been built and distributed to the mass market. Detecting and preventing errors or bugs is a significant difficulty for development teams. Early error detection demands regular monitoring throughout the development life cycle.
Trained AI and ML algorithms can assure error-free testing in less time than manual testing, allowing code testers to focus on other critical duties like code maintenance. AI-powered code testing prototypes enable development teams to perform mass testing on thousands or millions of lines of code. Development teams can handle case-specific tests, while AI-assisted automation solutions can handle repetitive and time-consuming tests. This ultimately leads to reduced errors because AI-assisted tests scope and repair faults with extreme precision, resulting in a rise in overall software quality improvement.
DeepCode, for example, uses AI-enabled cloud testing to help developers quickly test and deploy functioning code. Software testers utilise a template test plan with parameters and then run the code through an AI tool, which turns the code into a functional test case and addresses specific errors. Engineers are immediately issued permission requests for problem fixes and eventual production deployment. This saves time, money, and resources and results in a high ROI for the company.
Coding a big and complicated project with several stakeholders is generally time-consuming and labour-intensive. AI coding assistance may significantly decrease development teams’ efforts while enhancing productivity. Intelligent apps like Kite for Python can assist with common but critical coding activities, saving time and reducing coding errors. This AI-powered intelligent programming assistant analyses previous mistakes and faults found in software code to identify potential new issues in real time while new code is created. Developers may increase productivity by concentrating on more creative and strategic activities, such as increasing user experience.
Improve Decision Making
Software developers invest significant time and money deciding which features to include in a product. By assessing the performance of existing applications and prioritising products and features for future development, AI helps accelerate decision-making. Software firms can make data-driven business choices at scale fast, boosting market impact and improving revenue.
Software development teams can use the AI Canvas tool to evaluate how AI can help them make tactical decisions. The canvas assists development teams in organising and identifying critical questions and viability challenges associated with developing and deploying AI/ML models. AI may also examine the success of previous development projects and products, assisting software organisations in allocating important resources to initiatives and delivering higher ROI.
Bottomline
Artificial intelligence has huge potential to influence the future of software development. AI-enabled solutions enable software businesses to provide customer-driven experiences by tailoring application strategies to business requirements. AI can also significantly impact your business and projects, so always hire a reputable software development services provider to create faultless and effective software solutions.
