Time to read: 6 minutes | Published: March 10
AI stack What is an AI stack?
An AI stack refers to the collection of tools, technologies, and frameworks that work together to build, train, deploy, and manage AI applications. It encompasses everything from data processing and machine learning frameworks to cloud services and hardware infrastructure, enabling developers and organizations to effectively create and scale AI solutions.
Examples of products within the AI stack:
TensorFlow – An open-source machine learning framework that allows developers to build and train deep learning models.
AWS Sagemaker – A cloud service provided by Amazon Web Services that simplifies the process of building, training, and deploying machine learning models at scale.
- Overview of an AI stack
- Infrastructure layer
- Data management layer
- Inference and deployment layer
- Application layer
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What goes into an AI stack?
Here's a high-level breakdown of the different layers within the AI stack:
- Data collection and preparation: This is the foundation of the AI stack. It involves gathering raw data from various sources and cleaning, organizing, and preparing it for use in AI models. Tools and platforms at this layer help automate data pipelines and ensure data quality.
- Data storage and management: This layer handles the storage, organization, and accessibility of massive datasets. Solutions here often include databases, data lakes, and cloud storage services that enable efficient data retrieval and management.
- Model development and training: At this layer, developers create and train AI models using machine learning frameworks and libraries. Tools in this category, such as TensorFlow and PyTorch, allow data scientists to experiment, train, and fine-tune their models using structured and unstructured data.
- Model deployment and serving: This layer involves taking trained models and deploying them to production so they can be used in real-time applications. Platforms and services here focus on scaling, monitoring, and managing the performance of models, such as AWS Sagemaker or Kubernetes-based solutions.
- Infrastructure and compute: This is the backbone that powers the AI stack. It includes the hardware (e.g., GPUs, TPUs) and cloud services that provide the computational power needed for training complex models and running AI applications at scale.
- Monitoring and optimization: Once models are in production, this layer ensures they perform efficiently and consistently. Monitoring tools track metrics, detect anomalies, and identify when a model needs retraining. Optimization solutions also adjust resources and fine-tune models for maximum performance.
- User interfaces and integration: The final layer is where AI systems connect with users and other business systems. This includes APIs, dashboards, and software tools that make the AI outputs accessible and actionable for decision-making and operational use.
Each layer of the AI stack plays a crucial role in building a robust and scalable AI ecosystem, enabling businesses to leverage AI effectively from data collection to end-user integration. We will go further into detail in what each step does.
