Serving

Serving is the process of delivering processed data or model predictions to end-users or applications in real time. In the context of machine learning and data science, serving involves deploying a trained model and making its outputs accessible through an API or another interface. This enables applications to utilize the model's predictions or insights in production environments, ensuring efficient and scalable data delivery.

Also known as: Model deployment, Data delivery, API serving, Inference serving, Online serving, Real-time processing, Model serving.

Comparisons

• Serving vs. Training: Training involves creating a model by learning from data while serving involves using the trained model to provide predictions or data insights.
• Serving vs. Batch Processing: Serving provides real-time responses, whereas batch processing involves processing large volumes of data at scheduled intervals.

Pros

• Real-Time Delivery: Provides immediate access to model predictions or processed data.
• Scalability: Can handle a high volume of requests efficiently, supporting large-scale applications.
• Integration: Easily integrates with various applications through APIs, enhancing usability.

Cons

• Complexity: Requires robust infrastructure to ensure low latency and high availability.
• Maintenance: Continuous monitoring and updating are necessary to maintain performance and accuracy.
• Resource Intensive: This can be resource-intensive, requiring powerful servers and optimized code to manage real-time demands.

Example

In a recommendation system, serving refers to the deployment of a trained recommendation model that provides real-time product recommendations to users based on their browsing history and preferences.