What is an AI stack?

Similar to technology stacks (or tech stacks) in software development, an AI stack organizes the elements into layers that work together to enable efficient and scalable AI implementations. This layered approach breaks down the complex process of building AI solutions into manageable components, enabling teams to focus on individual aspects without losing sight of the bigger picture.

Each layer in the stack represents a specific function, from data handling to model deployment, making it easier to identify dependencies, allocate resources and address challenges systematically. This modular view enhances clarity, especially when working in multidisciplinary teams, as it creates a shared understanding of how various components interact.

Different AI applications will touch multiple layers in the AI stack. For example, Red Hat® OpenShift® is an enterprise Kubernetes platform designed to manage containerized applications at scale that is used across virtually all of the layers of the AI stack.

Various players in the AI space organize the AI stack differently, arranging the components in a different order or emphasizing different components or functions. This is because approaches to AI can vary, both at the use case level and at the organizational level. Also, the AI development landscape is constantly evolving.

Next, is a generalized version of an enterprise AI stack. You can learn more about IBM’s approach to generative artificial intelligence (gen AI) and large language models (LLMs) (think OpenAI’s GPT) by reviewing IBM’s generative AI tech stack.

The AI infrastructure layer forms the foundation upon which AI systems are built and deployed. It provides the computational power, physical storage and tools necessary to develop, train and operate AI models effectively. This layer supports the entire AI lifecycle, from initial experimentation to large-scale deployment, and is made up of a few different key components.

Physical hardware is needed to process data. Chips can be optimized for AI workloads: high-performance processing units called AI accelerators—GPUs, CPUs and TPUs—dramatically reduce training time for complex models. Also, distributed computing enables the development of cutting-edge, resource-intensive systems such as large language models.

Cloud services platforms (for example: AWS, Microsoft Azure, Google Cloud and IBM Cloud®) provide the flexibility to scale resources up or down, making it accessible for businesses of all sizes, while edge compute empowers real-time decision-making in remote or low-bandwidth environments. The compute layer integrates closely with orchestration tools, optimizing resource allocation and helping to ensure cost efficiency.

Physical storage systems must handle vast amounts of data used throughout the AI lifecycle, from raw data sets to model weights and logs. High-throughput storage solutions allow for rapid data access, which is essential for computationally intensive tasks such as training deep learning models.

Scalability is another key feature, with distributed file systems such as HDFS or object storage systems (Amazon S3) supporting growing data demands. These systems often employ tiered storage strategies, keeping frequently accessed data on high-speed media while archiving less-used data on slower, more cost-effective solutions.

Robust backup and recovery mechanisms further encourage data resilience, combining local and cloud storage options to protect against failures.

AI often involves moving lots of data from one place to another with minimal latency. Networking complements storage by connecting the various infrastructure components, enabling smooth data transfer and collaboration.

This is another foundational part of the AI stack, focusing on collecting, storing and preparing data for AI models. It includes databases, data lakes and data warehouses. Data scientists use various tools for data ingestion, cleaning and preprocessing, which are also part of this data management layer.

High-quality, well-prepared data allows models to learn effectively, leading to better predictions and decision-making. Conversely, poor-quality or biased data can compromise the accuracy and fairness of AI models, resulting in suboptimal outcomes. By investing in a robust data layer, organizations set the stage for successful AI implementations.

Data can be ingested from various sources, such as structured databases, unstructured text files, images, IoT devices, application programming interfaces (APIs) or user interactions. The storage infrastructure must be capable of handling large volumes of diverse data while maintaining reliability and accessibility.

Technologies include relational databases (for example: MySQL and PostgreSQL), NoSQL databases (for example: MongoDB and Cassandra) and data lakes (for example: Hadoop) for handling structured and unstructured data.

Ingestion involves importing data from various sources into storage systems. Tools such as Apache Kafka automate and manage data ingestion pipelines, helping to ensure that data flows smoothly into the system.

Raw data often requires cleaning, normalization and transformation before it can be used in AI models. This involves removing duplicates, filling missing values, standardizing formats and encoding categorical variables.

The programming language Python features free libraries for this purpose, and others, including Pandas, NumPy or tools such as Apache Spark are also commonly used for preprocessing.

For supervised learning, data often needs to be labeled for the model to learn. This involves tagging images, categorizing text or marking relevant features. Platforms such as Labelbox, Amazon SageMaker Ground Truth and open source tools such as LabelImg facilitate annotation workflows.

Data storage and data processing must adhere to privacy laws (for example: GDPR and CCPA). Encryption, access control and anonymization techniques are often employed to protect sensitive data.

This is where AI models are designed, trained and fine-tuned to solve specific problems, determining the core functions and intelligence of an AI system. It builds upon the data layer by using processed and cleaned data to train algorithms capable of learning patterns, making predictions or generating outputs.

This layer also establishes a feedback loop with the data layer, enabling retraining and improvement as new data becomes available. This layer is central to the AI lifecycle, as it defines how well the system performs in real-world applications.

Machine learning (ML) and deep learning frameworks simplify model creation and training. Popular tools include TensorFlow, PyTorch, Scikit-learn, Keras and XGBoost, each suited for different types of AI tasks, such as computer vision, natural language processing (NLP) or tabular data analysis.

Choosing the right machine learning algorithm is key to achieving optimal performance. Algorithms range from linear regression and decision trees for simple tasks to complex architectures such as neural networks and transformers. The selection depends on factors including the type of data, the problem domain and computational constraints.

Training involves feeding labeled data into the model so it can learn patterns and relationships. This step requires significant computational resources for complex models. The training process involves setting hyperparameters (for example: learning rate and batch size) and iteratively optimizing the model by using techniques such as gradient descent.

Feature engineering transforms raw data into meaningful inputs for the model. This step might include scaling, encoding, dimensionality reduction or creating new derived features.

Using pretrained models, such as BERT and ResNet, can significantly reduce development time and computational costs. Transfer learning adapts these models to new tasks with minimal additional training.

After models are developed, they often need optimizing and fine-tuning before deployment. This might include hyperparameter tuning, model compression and model validation.

Before deployment, models are evaluated using separate validation and test data sets to measure performance metrics including accuracy, precision, recall and F1 score. This step helps to ensure that the model generalizes well and performs reliably on unseen data.

The model deployment layer is where machine learning models change from development to practical use, delivering predictions or inferences in live environments.

Deployment involves packaging models into deployable formats, often using containerization technologies, promoting consistency and portability across different environments. These containers are then managed and scaled using orchestration platforms, enabling load balancing, fault tolerance and high availability.

The deployed models are typically exposed through APIs or microservices using frameworks such as TensorFlow Serving, NVIDIA Triton or custom-built solutions, enabling seamless integration with business systems, mobile apps or web platforms.

The application layer is where AI models are integrated into real-world systems to deliver actionable insights and drive decision-making, making it the most user-facing part of the AI stack. This layer embeds AI capabilities into software applications, products and services.

At this stage, AI models become part of business logic, automating tasks, enhancing workflows or powering intelligent features, such as recommendation systems, predictive analytics, natural language processing or computer vision. These capabilities are typically accessed through APIs or embedded into microservices, encouraging seamless interaction with other components of the application ecosystem.

A key focus of the application layer is usability. AI functionality is often wrapped with intuitive user interfaces (UI) that use visualizations and other presentations to communicate information in a clear, interpretable manner, enabling users to understand and act on AI-driven insights.

For instance, a fraud detection AI might flag suspicious transactions within a financial platform and generate a notification through automation, while a chatbot user experience interacts with users in real-time.

The observability layer facilitates monitoring, tracking and evaluation of AI workflows. It provides the visibility and insights needed to understand how AI models perform in real-world environments, enabling teams to identify and resolve issues promptly, maintain system health and improve performance over time.

At the core of the observability layer are tools and frameworks that track various metrics related to both the AI models and the infrastructure on which they run.

The governance layer is the overarching framework that helps to ensure that AI systems are deployed, used and maintained responsibly, ethically and in alignment with organizational and societal standards.

This layer is crucial for managing risks, promoting transparency and building trust in AI technologies. It encompasses policies and processes to oversee the lifecycle of AI models with legal regulations, ethical principles and organizational goals.

A primary function of the governance layer is establishing data collection and use policies along with compliance frameworks to adhere to regulations such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) or AI-specific guidelines including the EU AI Act. These frameworks define how data is collected, stored and used, encouraging privacy and security.

Also, governance includes creating mechanisms for auditability and traceability, enabling organizations to log and track AI decisions, model changes and data usage, which is critical for accountability and addressing disputes or errors.

The governance layer also addresses issues of fairness, bias and explainability in AI systems. It involves implementing tools and techniques to detect and mitigate biases in training data or model outputs, helping to encourage AI systems to operate equitably across diverse populations.

Viewing AI as a stack promotes scalability, flexibility and efficiency. Teams can work on upgrading specific layers to benefit from the latest advancements without overhauling the entire system, enabling iterative improvements and adaptations as technologies and business needs evolve.

For instance, you can switch from one cloud provider to another in the infrastructure layer or adopt a new machine learning framework in the model development layer without disrupting the application.

This layered perspective also makes it easier to benchmark and monitor each stage of the AI lifecycle, helping to ensure that performance, compliance and reliability are maintained at every step. The stack approach simplifies the complexity of AI, making it more accessible and actionable for organizations of all sizes.