What Is an AI Tech Stack?
A technology stack for artificial intelligence is a complete, end-to-end solution that consists of hardware, software, and tools that facilitate the development and deployment of AI applications. While similar to the technology stack used to build general-purpose software applications, the AI tech stack includes specialized tools to support the building of AI models that enable machine learning and deep learning.
The AI tech stack includes four key foundational layers: the application layer, the model layer, the data layer, and the infrastructure layer.
The Application Layer
The application layer of the AI tech stack includes any software, user interfaces, and accessibility features that enable users to interact with the underlying AI models and the datasets that power an AI solution. For example, browser-based interfaces allow users to send questions to a generative AI model like ChatGPT, or a data analytics suite might provide visualizations in the form of graphs and charts to help users understand the AI model’s results.
The Model Layer
The model layer of the AI tech stack is where AI models are developed, trained, and optimized. AI models are developed using a combination of AI frameworks, toolsets, and libraries and are subsequently trained on vast amounts of data to help refine their decision-making processes.
The Data Layer
This layer focuses on dataset collection, storage, and management, interfacing with and enabling all the other layers. Data from this layer is fed to the model layer, new data from the application layer is captured here for future model analysis, and the infrastructure layer provides the resources needed to scale, secure, and reliably process the data.
The Infrastructure Layer
The infrastructure layer of the AI tech stack includes all hardware and compute resources needed to run the AI models and any user-facing software. This can include enterprise data centers, cloud servers, client devices such as laptop and desktop AI PCs, or edge devices such as sensors and smart cameras.
AI Workflows
Successfully deploying an AI tech stack depends on AI workflows, which are the sequential steps to develop and train an AI model that follows the order of data, model, and deploy:
- The data step includes any formal data gathering or generation processes, usually followed by preprocessing and storage. This step prepares data for use by an AI model, either for training or inference.
- The second step, AI modeling, involves developing an AI model through the layering of algorithms to create a neural network that simulates the logical and decision-making patterns of the human mind.
- The final step, deploy, occurs when the AI model is deployed in a real-world use case, such as a personalized AI chatbot that answers user queries with minimal or zero human intervention.
AI Solution Components
Many of the underlying components of an AI tech stack will be familiar to most professionals who work with technology and include hardware, networking, and software. However, AI tech stack components and tools are specifically designed to handle the unique requirements and demands of AI workloads.
AI Hardware
AI hardware includes any silicon or bare metal components that make up an AI deployment and form the foundational layer of the AI tech stack.
CPUs
The central processing unit (CPU) or processor executes the logical instructions that make a computer run. Within a processor’s architecture, a single-threaded processor core can execute a single instruction at a time, while a multithreaded core can execute two instructions simultaneously. For AI workloads, multithreaded cores deliver higher performance than single-threaded cores and can help train AI models with greater efficiency.
Learn more about AI processors.
GPUs
Graphics processing units (GPUs) are specialized components designed to handle graphics workloads using a high number of execution units that operate in parallel. Graphics workloads and AI workloads depend on the same type of operations, which is why GPUs are commonly used in AI deployments. GPUs are available as discrete GPU (dGPU) plug-in cards or integrated GPUs (iGPUs) built into a CPU’s architecture. dGPUs are more common in AI servers, whereas iGPUs are more common in client computers or edge devices.
FPGAs
Field-programmable gate arrays (FPGAs) are separate plug-in cards for processing data whose functionality can be customized after manufacturing. This gives FPGAs a high degree of flexibility to help accelerate the movement, encryption, or processing of data for a variety of workloads, including AI.
Learn more about FPGAs for AI.
AI Accelerators
Discrete AI accelerators are specialized hardware components designed to process AI workloads and are ideal for scale-out data center deployments. Some discrete AI accelerators can offer features such as built-in networking to help reduce infrastructure costs while delivering AI training and inference performance comparable to GPUs.
Learn more about AI accelerators.
Edge AI
AI at the edge uses many of the same AI hardware components listed previously, including CPUs, GPUs, FPGAs, and AI accelerators. The key difference is where the systems are deployed. While many enterprises will deploy AI in their data centers, edge AI describes AI systems that are deployed in edge environments, such as city intersections, retail stores, and factory floors.
Edge AI inferences data at the point of generation for near-real-time analysis and action. Because of this, edge AI can potentially be more cost-effective than sending data to the cloud and back for inference, requiring less network infrastructure and fewer cloud-based resources.
AI Servers
AI servers are computers designed for AI workload requirements that provide services, applications, and data to other systems used by businesses and end users. AI servers combine AI processors, accelerators, and networking hardware to support data storage and preparation, as well as AI model training and inference. Enterprise data centers are made up of several AI servers, while edge AI deployments may rely on one or more edge AI servers to fulfill their purpose.
AI Networking
High-performance networking is a critical component of the technology infrastructure that enables AI applications to operate efficiently and securely. Networking for AI must provide reliable connections that are robust, efficient, secure, and flexible. AI can be implemented anywhere, so network solutions may include wired, wireless, and virtualized connections between and among disparate systems and devices in the AI data center, client, cloud, and edge. Robust network security capabilities are essential, as well, to protect the enormous datasets, including users’ personal information, that feed machine learning algorithms and other AI programs.
AI Software
Like any other software, AI is code that runs on hardware. AI software can use many different coding languages, including Python, Java, or C++. Developers use AI frameworks to build, train, and deploy AI models, while AI frameworks make use of AI libraries and toolkits to make it easier to develop AI with prebuilt elements and code samples. AI software contributes to developing both the model and application layers of the AI tech stack.
AI Frameworks
An AI framework is a platform and is also considered an application. Developers use AI frameworks to build, train, and deploy AI solutions. AI frameworks combine different methodologies, support one or more coding languages, and may also provide an interface to make it easier to navigate and edit code. Examples of AI frameworks include PyTorch and TensorFlow.
AI Libraries
AI libraries are collections of modular, prebuilt AI functions and code samples that developers can use to develop their own AI models without needing to create them from scratch. AI frameworks use AI libraries to help accelerate the AI development process. Examples of AI libraries include Keras and Scikit-learn.