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Design Patterns in Python for AI and LLM Engineers: A Practical Guide
- Design patterns offer a clear structure to design more efficient and maintainable systems for AI and LLM engineers in Python.
- Design patterns work in three main categories - Creational Patterns, Structural Patterns and Behavioral Patterns.
- Singleton Pattern in Python is used to create a class with only one instance and provide access to that instance globally.
- Factory pattern in Python delegates the creation of objects to subclasses or dedicated factory methods for creating different types of models, data loaders or pipelines based on the specific context.
- Builder Pattern separates the construction of a complex object from its representation. It is useful for building pipelines, managing configurations, and readable objects that have many parameters.
- The strategy pattern allows switching between different strategies (inference or data processing) depending on the requirements.
- The observer pattern establishes a one-to-many relationship between objects in the real-time monitoring of events, data synchronization and metrics.
- Design patterns have unique characteristics in AI engineering as they involve dynamic workflows rather than just managing a database and user session.
- When implementing design patterns in AI engineering, one should consider the scalability and performance implications.
- Using best practices such as not over-engineering, documentation and testing can help improve code organization and make it more maintainable.
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