What do inference pipelines consist of?

Inference pipelines are designed to streamline the process of making predictions from a trained model by organizing the workflow into a series of reusable steps. This approach allows for better modularity and flexibility, as each step can be reused across different models or applications. By constructing the pipeline with a sequence of generative steps, it enhances the efficiency of the overall inference process, ensuring that each component performs its designated role in a cohesive manner.

This structure not only facilitates the management of complex data processing tasks but also aids in maintaining consistent results by applying the same sequence of operations to varying datasets. The modular nature of these pipelines is particularly beneficial, as practitioners can easily modify or optimize individual steps without the need to overhaul the entire inference structure. This adaptability is crucial for effective model deployment and inference in real-world applications.