Definition
Building refers to the end-to-end engineering process of creating AI solutions, which includes data collection, model selection, training, validation, and deployment. It encompasses the technical infrastructure required to support machine learning workflows, such as cloud computing resources, version control for models, and monitoring systems. Effective building ensures that theoretical models are transformed into reliable, scalable, and maintainable software products.
Summary
The practical phase of developing, training, and deploying AI models and systems from initial design to production readiness.
Key Concepts
- Development Lifecycle
- Infrastructure
- Deployment
- Engineering
Use Cases
- Constructing a recommendation engine for an e-commerce platform.
- Setting up MLOps pipelines for continuous integration and delivery.
- Integrating computer vision models into mobile applications.