Artificial intelligence (AI) and machine learning (ML) are now a staple across many different industries. Some segments have actually skyrocketed thanks to them, while others are still in the process of transformation. The challenging field of software development is no exception. As it is a complex and intricate process, it was only a matter of time before we could make some development tasks more efficient with the help of artificial intelligence.
Will AI replace software development?
None of us can predict the future and therefore it is impossible to say how the development of artificial intelligence will affect software development. While AI development is advancing by leaps and bounds, it is unlikely that AI will be able to fully replace a process as complex as software development.
It’s not just about writing code, but also about having a creative and logical grasp of business strategy, user behavior, and a certain amount of agility in responding to potential new problems, to which the entire range of processes must subsequently be adapted.
But what we do already know is how to use artificial intelligence for sub-tasks, automating them and making them more efficient. There are already several tools out there that allow developers to leave the repetitive sub-tasks to AI and focus on the strategic and creative aspects of software development.
We even use some of these when working on custom development for our clients.
Top AI tools for software development
Let’s break down the various AI tools that are currently being actively used for software development in 2023 and may become a stable addition throughout the software development process in the future.
Microsoft Azure AI Platform
It is a well-known cloud platform that provides solutions and services for developing, training, and deploying custom AI models. You will find that it contains many pre-built features, including:
- Azure Cognitive Services, which provides a collection of APIs for natural language processing, image analysis, and speech or object recognition.
- Azure Bot, which has the ability to create and deploy AI chatbots with predefined templates.
- Azure Databricks, an integration of the Apache Spark big data service with Azure ML machine learning, which is used for big data mining.
- The Azure AI platform supports the popular Python programming language.
- Azure GitHub Actions provides a wealth of documentation, tutorials, and ready-made workflows.
Google Cloud AI Platform
Another cloud platform that provides multiple ways to create, train and host your own AI models in the cloud is AI Platform, only this time powered by tech giant Google.
- The platform offers pre-configured virtual machine (VM) capabilities to assist in the development of machine learning applications.
- Cloud Natural Language API allows you to analyze and process unstructured text and extract essential and/or desired information from it.
- Cloud Vision API provides advanced real-time image and video analytics, allowing developers to detect and recognize objects, logos, faces and text from images or videos.
- Cloud Speech API is a powerful speech-to-text tool that works in a variety of languages and accents.
- Also available is a suite of open-source tools called Kubeflow, which makes it easier to build and deploy machine learning systems.
This is an open-source machine and deep-learning platform. This tool offers a wide range of features, including machine learning algorithms and statistical and linear models, as well as risk analysis and prediction modeling.
- AutoML is a feature within H2O that automates the process of creating a large number of different models in order to automatically find the best one according to specified criteria.
- It allows you to use programming languages such as R and Python.
- H2O Flow is a visual tool for interactive model creation and editing. It allows users to visually explore, add, edit and manage data and models.
- One of the advantages of H2O is that it has a myriad of different machine learning and neural network models at its disposal.
- H2O makes it possible to distribute machine learning tasks across multiple computers, thereby increasing the performance and speed of computation.
This AI tool comes courtesy of IBM and has advanced AI features, but most notably, various capabilities for recognising and analyzing conversations in both text and spoken form. You can also find APIs for mobile apps and machine-learning interfaces.
- Watson Studio allows you to train, deploy and manage your own AI models, and it also enables you to prepare and analyze information within one integrated environment.
- Watson Assistant is characterized by its ability to build AI chatbots and APIs for virtual assistants which can be used within mobile phone apps.
- Watson Knowledge Catalog helps you collect important information and access it more easily with AI
- With Watson Machine Learning, you can use your own data to build, train, and deploy machine and deep learning models.
- Watson IoT can create and maintain an efficient IoT infrastructure and interconnect the various elements.
Here is a software library with open-source codes for machine learning and artificial intelligence. The platform offers management, development, and training of a number of systems using APIs.
- TensorFlow offers tools for visualizing and tracking models. It also allows you to experiment with them and optimize them.
- It can train and process a large volume of data in a short time.
- You can deploy AI solutions seamlessly across mobile, desktop and other peripheral devices.
This is another perfect tool that allows you to create powerful AI-driven applications and cutting-edge machine-learning training models.
- The Nia Advanced Machine Learning Platform offers a wide range of high-performance and scalable algorithms that enable you to build robust and powerful machine-learning models.
- Nia Chatbot allows you to create AI-powered chatbots and automate your interface thanks to access to a variety of databases.
- With Infosys Nia, you benefit from ready-made structures and technologies for more efficient data processing, which are essential for machine learning and AI.
- Infosys Nia includes high-performance tools for processing large volumes of data.
- Nia provides tools to predict possible future events and behaviors based on data analysis.
Choosing the right tools is essential
In software development, each project has its own specifications and requirements. This is why it is important to adapt appropriate frameworks and tools so that they do not unnecessarily complicate the process, and instead make it more efficient.
These tools are merely pieces of the jigsaw puzzle in the overall software development process. It’s only in the hands of a skilled and experienced developer or engineer that any AI solution gains added value.
If you want to use the latest AI and machine learning technologies in your solutions, arrange a consultation with us and we’ll be happy to help you make the right choice.
However, if you want to leave the entire development to an external partner, we can handle the entire process from problem identification, through the selection of tools, to the development itself. We can also enhance your IT team with our professionals who can support you in the areas of AI and machine learning.