AI is transforming the world of software development, enabling developers to create applications that can perform previously impossible or impractical tasks. AI-based applications can leverage machine learning, natural language processing, computer vision, speech recognition, and other AI techniques to provide intelligent solutions for various domains and use cases.
However, building AI-based applications is a challenging task. It requires a combination of technologies, frameworks, and tools to support AI systems’ development, deployment, and maintenance. These components form the tech stack of an AI-based application, which can vary depending on the specific requirements and objectives of the project.
In my role at work, I am starting to see organisations investing in building AI-based applications, and I have started to gain some insights into what tech stacks are being considered. I thought it would be worth exploring some of the most popular tech stacks for building AI-based applications and explaining why they are popular among developers and organisations. I will also discuss some of the unique needs of AI and how they affect the choice of technologies used in the tech stack.
What is a tech stack?
A tech stack refers to a set of technologies, frameworks, and tools to build and deploy software applications. A comprehensive tech stack is crucial in building effective AI-based applications, which include various components, such as machine learning frameworks, programming languages, cloud infrastructure, and data processing tools.
A typical tech stack for an AI-based application consists of four layers:
- Application layer: This layer includes the end-user interface and functionality of the application, such as web pages, mobile apps, chatbots, and voice assistants. The application layer interacts with the user and provides the desired output based on the input.
- Model layer: This layer comprises the AI models that power the application, such as neural networks, decision trees, and natural language models. The model layer processes the input data and generates the output data for the application layer.
- Data layer: This layer encompasses the data sources and pipelines that feed the AI models with relevant and high-quality data. The data layer involves data collection, storage, preprocessing, transformation, labelling, and annotation.
- Infrastructure layer: This layer encompasses the hardware and software platforms running AI models and applications. The infrastructure layer involves cloud services, servers, GPUs, CPUs, and containers.
What are some popular tech stacks for AI-based applications?
There is no one-size-fits-all tech stack for AI-based applications. Different projects may have different goals, challenges, constraints, and preferences that influence the choice of technologies. However, some tech stacks have gained popularity among developers and organisations due to their performance, scalability, reliability, security, cost-effectiveness, and ease of use advantages. Below are some examples of popular tech stacks for AI-based applications.
Python + TensorFlow + Google Cloud Platform
Python is one of the most widely used programming languages for AI development due to its simplicity, readability, versatility, and rich ecosystem of libraries and frameworks. TensorFlow is one of the most popular machine learning frameworks that enables developers to build and train complex neural networks using a high-level API or a low-level API. Google Cloud Platform (GCP) is a leading cloud service provider that offers various products and services for AI development and deployment, such as Cloud ML Engine, Cloud Vision API, and Cloud Speech-to-Text.
This tech stack is popular because it offers a comprehensive, integrated solution for building scalable and robust AI-based applications. Python and TensorFlow provide a flexible and powerful platform for developing custom AI models with state-of-the-art techniques. GCP provides a convenient and cost-effective platform for running and managing AI models and applications with high performance and reliability.
Java + Apache Spark + AWS
Java is another popular programming language for AI development due to its portability, performance, and compatibility with various platforms and frameworks. Apache Spark is a distributed computing framework that enables developers to process large-scale data sets using machine learning, graph processing, streaming analytics, and other techniques. Amazon Web Services (AWS) is another leading cloud service provider that offers various products and services for AI development and deployment, such as Amazon SageMaker, Amazon Rekognition, and Amazon Polly.
This tech stack is popular because it offers a scalable and reliable solution for building data-intensive and distributed AI-based applications. Java and Apache Spark provide a fast and efficient platform for developing complex AI models with large-scale data processing capabilities. AWS provides a secure and flexible platform for running and managing AI models and applications with various features and options.
What are some unique needs of AI, and how do they affect the choice of technologies?
AI-based applications have unique needs that distinguish them from other types of software applications. These needs include:
- Data: Data is the fuel of AI, as it is used to train, test, validate, and improve AI models. Data quality, quantity, diversity, availability, and security are crucial factors affecting AI models’ performance and accuracy. Therefore, choosing technologies that can handle large-scale and complex data sets with efficient and reliable data processing tools is essential for AI development.
- Computing power: AI models, especially deep neural networks, require a lot of computing power to perform complex mathematical operations on large amounts of data. Therefore, choosing technologies that can leverage hardware acceleration, such as GPUs, TPUs, or FPGAs, is important for AI development. Moreover, choosing technologies that can scale up or down the computing resources according to the demand is also essential for AI deployment.
- Model Management: AI models are not static but dynamic and evolving entities that need to be updated, monitored, evaluated, and optimised continuously. Therefore, choosing technologies that can support model management tasks, such as versioning, testing, debugging, logging, auditing, and deploying, is important for AI development.
- Ethics: AI models can significantly impact society, the economy, and the environment, influencing human decisions, behaviours, and outcomes. Therefore, choosing technologies that can ensure the ethics of AI models, such as fairness, accountability, transparency, and explainability, is vital for AI development.
These unique needs of AI affect the choice of technologies used in the tech stack in various ways. For example:
- Python is preferred over Java for AI development because it has more libraries and frameworks that support data science and machine learning tasks.
- TensorFlow is preferred over Apache Spark for AI development because it has more features and options that support deep learning and neural network tasks.
- GCP is preferred over AWS for AI deployment because it has more products and services that support hardware acceleration and model management tasks.
This post is a very simple summary of the most popular tech stacks for building AI-based applications I have encountered while working with customers. It provides a brief explanation of why they are popular among developers and organisations. Hopefully, it has helped you understand some of the unique needs of AI and how they affect the choice of technologies used in the tech stack.
Please comment below if you have any questions, feedback, or observations on what you are seeing in the AI development field. Thank you for reading!