AI Programming: Core Concepts and Languages for AI Software Development

Machine learning (ML), neural networks, natural language processing (NLP) and other types of artificial intelligence (AI) rely on AI programming languages to function. But programming languages are only one component necessary to build an AI system. In this article, we’ll explain what’s required to build an AI system and highlight five leading AI programming languages to consider for your next AI software development project.

What is required for building an AI system?

Artificial intelligence systems are made up of many different parts. Successfully building, training and integrating an intelligent system into a production-ready product requires careful orchestration of these component parts. 

Data 

The primary ingredient in any AI system is data. An AI system’s performance is based on the data its model or models are trained on. For this reason, training data must be accurate, reliable and representative of the problem the system is attempting to solve. Another consideration is the quantity of data. The amount of data needed for a particular AI system depends on the complexity of the task and the model architecture. Insufficient quantities of data for the use case will hinder performance. Data diversity—covering a wide range of scenarios, demographics and use cases—is also crucial since a lack of diversity can result in a model that performs well on the training data but poorly on new data. Data diversity can also help better address ethical concerns around AI.

Data storage

A reliable, cloud-based data storage and management system is essential for AI development. With cost-effective, near-infinite storage capacity, cloud-based storage solutions can accommodate the massive quantities of data AI applications require for training and validation processes. Cloud data platforms that are built for AI development offer support for structured, semi-structured and unstructured data, as well as comprehensive data security controls and governance. 

Compute resources

AI models consume large amounts of compute resources, requiring processors optimized for these systems. The processor powers the computer program that executes the arithmetic, logic, and input and output commands that make the system work. Fast processors drastically reduce the amount of time required to train a model and support it while in production. There are two main types of processes used in AI: central processing units (CPUs) and graphics processing units (GPUs). CPUs are commonly used to train many of the traditional machine learning models, while GPUs are best equipped for training deep learning models and large language models (LLMs), and for powering algorithms using visuals. 

Data processing framework

Raw data isn’t suitable for AI systems. Before data can be used for AI, it must be processed, cleaned, transformed and structured to fit the use case. A data processing framework is a tool that handles the complex, large-scale data transformations involved in building, training and deploying AI systems. Data processing frameworks leverage distributive processing—the practice of spreading complex computing tasks across multiple machines to accelerate the data transformation process.

Machine learning libraries and frameworks

Libraries are a collection of prewritten code used to accomplish a specific task. In the context of AI software development, libraries are used for a range of functions including data visualization, data analysis, model training and debugging. Machine learning frameworks are more comprehensive than libraries. An ML framework is a collection of libraries, pre-built modules, APIs, data processing functions, model training tools and other capabilities, such as GPU acceleration.

Human talent

Behind every AI software development project is a team of talented professionals. Although team members may vary by project, core members include data scientists, data engineers, ML architects, DevOps engineers, software developers and domain experts. Each plays a vital role in the ML development life cycle.

MLOps platform

MLOps, or machine learning operations, platforms are comprehensive tools for managing the MLOps process, automating the entire development cycle from data collection to deployment to production. From data collection and model training to deployment and monitoring, MLOps platforms provide many essential capabilities including version control for models, automated training and deployment pipelines, model performance tracking, and collaboration tools.

LLMOps

LLMOps, or large language model operations, manage and optimize large language models, including deployment, fine-tuning and performance-monitoring functions. LLMOps teams focus on maintaining efficiency, accuracy and ethical usage across diverse natural language processing applications.

Top AI programming languages

Artificial intelligence programs use complex algorithms that require specialized programming languages and frameworks. Although Python has become the dominant language for general AI development, other AI programming languages have distinct advantages for specific use cases.

Python

It’s impossible to place Python anywhere except at the top of the list when it comes to AI programming. With its low barrier to entry, high readability, platform independence and large community of active users, Python is well-suited for a wide range of AI software development projects. From data analysis to deep learning, Python’s vast AI and ML-focused library ecosystem, combined with its strong data handling and visualization capabilities, have made it the AI language of choice for many users.

R

R is another popular AI programming language. It excels at handling very large datasets and is well-suited to numerous AI processes, including exploratory data analysis, data manipulation, feature engineering and the creation of predictive models. Like Python, R has an extensive collection of packages that support AI software programming projects.

C++

Developed in 1979, C++ is one of the oldest programming languages still in use. Despite its age, it’s a viable option for AI software development. C++ remains a flexible, fast and efficient language, making it ideal for working with data-heavy, computational-intensive tasks. The granular control it provides for memory management makes it a popular choice for use in embedded systems such as Internet of Things (IoT) devices and in real-time use cases where efficiency and speed are paramount.

Julia

Compared to Python and R, Julia is a relative newcomer to the field of AI programming. Julia offers a unique value proposition for AI software development—pairing ease of use and speed. Like Python, Julia has a very user-friendly syntax that’s written in a way that’s similar to English. Its high readability makes it relatively simple to learn and use.  But unlike Python, it's a compiled language, making it much faster, especially when working with the massive amounts of data used in AI systems. Julia’s AI and ML-specific use cases include predictive modeling, deep learning and neural networks. 

Java

Java’s dominance in mobile app development makes it an important player in AI programming as well. It’s an easy-to-use, performant language that provides enhanced security and easy debugging. Java, like Python, is platform-independent, allowing it to be used in a variety of contexts. Java works with Tensorflow and comes with an impressive collection of libraries and frameworks dedicated to AI development. It’s well suited for building neural networks, machine learning applications and predictive analytics.

Advance your AI software development with Snowflake

Snowflake is unlocking new ways to drive innovation, improve productivity and derive more value from data, including new features for democratizing AI development.

The Snowflake AI Data Cloud provides the infrastructure needed to drive innovation with AI, including:

Data: With Snowflake for Data Lakes, build a transactional data lake architecture pattern for unified analytics, AI/ML and other collaborative workloads. In addition, Snowflake for Data Lakehouse allows the deployment of flexible architectural patterns with governed, optimized storage at scale.

Processing Framework: Snowflake Snowpark allows organizations to securely deploy and process non-SQL code in Snowflake using their language of choice for data pipelines, AI models and apps.

Snowflake Cortex (in private preview), is a new, intelligent, fully managed service that enables organizations to quickly analyze data and build AI applications—all within Snowflake. There’s no need for specialized AI expertise or a complex infrastructure to manage. Further, Snowflake Cortex provides the building blocks to create custom AI apps in the Data Cloud in minutes.