Deployment Patterns: Online vs. Batch Prediction

After a model is trained and packaged, a significant decision is determining how it will generate predictions for an application. This choice is not just a technical detail; it defines how users and systems interact with the model's intelligence. The two primary approaches for serving predictions are batch prediction and online prediction. Each serves different purposes and comes with its own set of trade-offs regarding cost, speed, and infrastructure.

Batch Prediction: Processing Data in Bulk

Batch prediction, also known as offline prediction, is a process where the model computes predictions for a large set of observations all at once. Instead of being available 24/7 to answer requests instantly, a batch process runs on a schedule, for example, once an hour or once a day.

Imagine you want to send a daily promotional email to customers who are most likely to purchase a specific product. There is no need to identify these customers in real-time. Instead, a process can run every night, analyze the day's customer activity, generate a list of target customers, and save that list for the marketing team to use the next morning.

This is the essence of batch prediction. The workflow typically looks like this:

1. Data Collection: Input data is gathered over time and stored in a database or a data lake.
2. Scheduled Trigger: A scheduler (like a cron job) initiates the prediction process at a predefined time.
3. Processing: A script loads the trained model, reads the entire batch of input data, and runs it through the model.
4. Storage: The model's predictions are saved to a database or as a file. Other applications can then access these pre-computed results without interacting with the model directly.

A typical batch prediction architecture. The model runs as a scheduled job, processing large volumes of data and storing results for later use.

When to Use Batch Prediction

Batch prediction is suitable when immediate predictions are not a business requirement. Common use cases include:

• Customer Segmentation: Grouping customers into segments based on their purchasing behavior over the last week.
• Product Recommendations in Emails: Generating personalized product recommendations to include in a daily newsletter.
• Risk Reporting: Calculating credit risk scores for all loan applicants at the end of the business day.

The main advantage of this pattern is high throughput and cost-efficiency. Since the job is not time-sensitive, you can use cheaper computing resources and process millions of records in a single run. The infrastructure is also simpler because you don't need a service that is always on and ready to respond. The downside is high latency; the time from when data is generated to when its prediction is available can be hours or even days.

Online Prediction: Serving Predictions on Demand

Online prediction, also called real-time or on-demand inference, is designed to provide predictions immediately. In this pattern, the model is deployed as a persistent service, often as a web API, that is always running and waiting for requests.

Consider a website that must decide whether to approve a credit card transaction. The decision must be made in milliseconds while the customer is waiting at the checkout. An application sends the transaction details to the model's API, and the model must return a "approve" or "deny" prediction almost instantly.

This is a classic use case for online prediction. The workflow is a direct request-response cycle:

1. API Request: A client application (like a mobile app or a web server) sends a single data point or a small group of data points to the model's API endpoint.
2. Real-Time Processing: The API service receives the data, immediately passes it to the loaded model, and generates a prediction.
3. API Response: The service returns the prediction directly to the client application in the API response.

An online prediction architecture where an application communicates directly with a model API service to get immediate predictions.

When to Use Online Prediction

Online prediction is necessary whenever an application requires an immediate response to function correctly. Common examples include:

• Fraud Detection: Analyzing a transaction in real-time to block it before it is completed.
• Dynamic Pricing: Adjusting the price of a ride-share service based on current supply and demand.
• Spam Filtering: Deciding whether an incoming email is spam before it lands in the user's inbox.

The primary advantage of this pattern is low latency. Predictions are available in milliseconds, enabling interactive user experiences. The main disadvantages are related to cost and complexity. It requires maintaining a highly available service that can handle traffic fluctuations, which is generally more expensive and operationally demanding than a simple scheduled job.

A Quick Comparison

Choosing the right pattern is a foundational step in designing your ML system. The decision is driven by your product's requirements for speed, scale, and cost.

Feature	Batch Prediction (Offline)	Online Prediction (Real-Time)
Latency	High (minutes, hours, or days)	Low (milliseconds to seconds)
Throughput	High (processes many records at once)	Low (processes one request at a time)
Data Volume	Large, bounded datasets	Single data points or small mini-batches
Infrastructure	Simpler (triggered scripts)	More complex (always-on, scalable API service)
Cost	Lower (uses compute resources on-demand)	Higher (requires constantly running servers)
Example Use Case	Daily customer churn prediction	Live language translation in a chat app

In practice, some complex systems may even use a hybrid approach. For instance, an e-commerce site might run a batch job every night to generate baseline product recommendations for all users. When a user logs in, an online model could then refine those recommendations based on what the user is clicking on during their current session.

Understanding these two fundamental patterns is an important part of planning a successful model deployment that aligns with your application's needs.