What Is AI Software?

AI software is a broad topic covering many different types of programs and tools used to prepare datasets and develop, deploy, and optimize AI models that enable machines to perform tasks involving learning, adapting, and decision-making.

Developers use AI software when working through the three stages of the AI pipeline or workflow—data preparation, model selection and training, and deployment—to help accelerate their productivity, make the process efficient, scalable, and manageable, increase deployment flexibility for the capabilities they are enabling, and optimize AI performance.

Role of Software in AI

AI, itself a type of software, requires the use of a wide range of software applications and tools to deliver its capabilities. Different types of AI software are required at different stages of the AI development process and vary based on the type of task that needs to be performed.

Data Preparation

The first stage of the AI workflow, data preparation, is the most time-consuming and crucial step. This stage involves getting data ready for ingestion by an AI model for training. Data scientists gather raw data, clean it, and collect it into databases where it can be analyzed and integrated into a unified dataset. This dataset can contain numerical data, videos, audio, and many other types of information unique to the intended application of the AI model.

Software tools used in this phase assist in collecting, storing, and managing data; extracting, manipulating, and validating data; and exploring, visualizing, and transforming data.

Model Development and Training

During this phase, an AI model—the software algorithm that will analyze the data, identify patterns, and make predictions—is selected and tuned for the use case and problem it is helping to solve.

The model is trained—meaning given the ability to detect specific patterns and perform specific tasks—on massive amounts of data and through iterations of ultrahigh-speed tests. The model learns as AI developers refine it until it can accurately and repeatedly complete the task it was designed for.

Purpose-built frameworks and libraries and prebuilt solution elements help aid and accelerate model selection, development, training, and evaluation.

The choice of software used depends largely on the type of AI model and whether it’s used for machine learning (ML) or deep learning (DL).

While AI models can be built from scratch, open source models, also known as foundational models, provide a starting point for AI capabilities. These models can be customized and fine-tuned to fit the specific needs of an AI solution more easily and quickly, saving valuable time for businesses racing against the competition.

Deployment

Once the AI model is fully trained and validated, it is integrated into a real-world environment during the deployment phase. Here, the trained AI model is put to work in an inference process where it must make decisions and predictions using new, unseen data, such as real-time footage in a computer vision application.

AI inference workloads are the engine of any AI application. They must run optimally for the AI application to function properly and are incredibly demanding on compute resources. This makes hardware infrastructure a critical choice during deployment to ensure scalability and predictability. AI software tools in this step are used to optimize how the model is served, how it performs, and to continually refine it.

Optimization

Optimization is an ongoing process. Monitoring software is used to continually analyze the performance of an AI application. Workload optimization and balancing software ensures the AI application has the ideal infrastructure to support it. The model also must be regularly updated and retrained as it scales and responds to real-world conditions.

The Impact of Hardware on Performance

AI solutions require a combination of AI software and AI hardware. Regardless of how well a model works in isolation, how it performs at scale and under the pressure of real-world scenarios is critical to AI’s use and the value it delivers. Successful AI implementations run on scalable, right-sized, secure AI infrastructure with optimized software. Learn more about the modern tech stack and the components of an end-to-end AI infrastructure that supports AI workflows.

Benefits of AI Software

AI software is critical to the entire AI workflow process, with each tool playing a specific role, and delivers overall benefits to the business, including:

• Improved productivity and efficiency: Libraries, frameworks, and prebuilt solution elements provide starting points for data scientists and developers, accelerating each workflow step. End-to-end platforms help to streamline the workflow and increase efficiency and productivity.
• Narrow the skills gap: An increasing number of end-to-end AI software platforms are available with tools that make it easier for non-data scientists to help train models, reducing the need to find and hire specialists and accelerating development and time to market.
• Scalability and flexibility: Deployment and optimization tools can help with a code-once, deploy-anywhere approach and make running models efficiently on different hardware and software platforms easier.
• Accelerate AI performance: Many tools are used to leverage hardware capabilities and optimize resource management and code efficiency to improve overall software application performance.
• Task automation: Automated code analysis, debugging, and testing frameworks can identify errors early in the development process, help ensure software is more reliable and secure, and reduce developers’ time on these tasks.

AI Software Solutions

By analyzing troves and streams of data, AI can reveal the best location for a new retail branch, predict a major machine malfunction, or reveal potential risks and growing threats. Getting AI to perform the specific tasks that make an organization more resilient, profitable, and innovative requires a number of AI software tools and components.

Generally, AI software tools employed by data scientists and software developers serve three primary functions:

• Optimizing resource management: AI is very resource intensive. Running AI inference requires a great deal of compute power, which can be costly both in terms of the hardware infrastructure and the energy used. Performance libraries, compilers, and kernels are used to leverage hardware capabilities and to improve software application performance by optimizing resource management and code efficiency.
• Simplifying the AI workflow process: AI libraries, tools, and frameworks are used to speed up and streamline the AI workflow process. Libraries provide collections of prebuilt functions, classes, and modules for building and training models. Frameworks provide an ecosystem of components and tools that facilitate the end-to-end process from data collection to post-deployment AI model management.
• Putting AI to use: Efficient AI applications that deliver ROI demand continual optimization and management, often across large organizations and many locations from edge to cloud. AI platforms and machine learning operations (MLOps) tools and principles unify AI management from end to end and help improve the productivity of cross-functional teams.

AI Libraries, Tools, and Frameworks for the AI Pipeline

The most important element of any AI initiative is having the right data for training and development. In fact, much of the time that goes into AI software development is spent exploring, cleaning, and preparing data. Common libraries and library accelerators are used to provide data structures and to enable data manipulation, analysis, and computing.

Model development and training is also very time and resource intensive, which is why developers rely on additional libraries and tools to simplify and speed up the process. The tools selected will vary based on the type of AI model and its intended purpose.

Choosing where and how to deploy AI models and inference has a major impact on the long-term success of any AI initiative. AI workload location needs to be optimized to control costs, protect data, and meet response time requirements. The deployment and optimization phase requires a number of inference engines, compiler frameworks, and toolkits to optimize and accelerate ML models and to enable them to run efficiently on different hardware and software platforms.

Moving Forward with AI Software Development

Develop faster with prebuilt models and reference kits

Existing AI models, called foundational models, provide a starting point to develop AI capabilities. Fine-tuning a foundational model is simpler and faster than building from scratch, saving valuable time for businesses racing against the competition.
AI reference kits—free, open source resources designed for specific use cases and preoptimized for popular frameworks and libraries—are also available to help developers and data scientists quickly prototype models, rapidly moving from concept to demo.

A new way to develop GenAI

Retrieval-augmented generation (RAG) offers a faster path to value from GenAI that avoids the need for additional model training. In the RAG methodology, foundational large language models are connected to specific knowledge bases and relevant context and information are integrated.

Everything you need from end to end

There are a number of AI software platforms that offer a complete ecosystem of tools, curated datasets, pretrained models, APIs, and libraries to streamline AI development, training, and deployment.