Batch vs Online Inference in Machine Learning
In this article, we look at the differences between Batch Inference and Online Inference in Machine Learning.
Table of Contents:
What is ML Inference?
First, let’s understand what inference is and how it works. Inference in machine learning refers to the process of using a trained model to make predictions or decisions based on new, unseen data. This is the stage where the model applies the knowledge it has gained during the training phase to perform its intended function, such as classifying an image, predicting a numerical value, or detecting an anomaly.
Inference vs Model Training
First, the training phase, a machine learning model learns from a labelled dataset by adjusting its parameters to minimise errors or maximise accuracy. This process involves algorithms like gradient descent and backpropagation (in the case of neural networks) to optimise the model’s performance. Second, after the model has been trained, it is then deployed to make predictions on new, unseen data – this is inference. Inference is the application of the model to this new data to generate outputs based on the patterns it learned during training.
How Does Inference Work?
- Input Data – firstly, the model receives new input data that it has not seen before. This could be a single data point or a batch of data points, depending on the application.
- Model Computation – secondly, the model processes this input through its layers or decision mechanisms, using the learned parameters from the training phase.
- Output Generation – and finally, the model generates an output, which could be a prediction, classification, or decision, depending on the task. This output then applies to the intended purpose, such as making a recommendation, driving a vehicle, or identifying a trend.
Challenges with Inference
- Latency – in real-time systems, low latency (the time it takes to generate a prediction) is critical. High latency can be a problem in applications that require immediate responses, like self-driving cars or live chatbots.
- Scalability – inference needs to scale efficiently with the amount of processing data, especially in batch processing or high-traffic applications.
- Accuracy – the accuracy of inference depends on how well the model’s training is and how well it generalises to new data. Poor training can lead to inaccurate predictions during inference.
What is Batch Inference?
Batch inference (also offline or static inference) is the process of making predictions on a large dataset all at once. Data is collected over a period, and predictions are made periodically, either on a scheduled basis or on-demand. This method is often applies in scenarios where predictions do not need to be made in real-time, allowing for the accumulation of data before processing.
For instance, consider a recommendation system used by an online retailer. The system might not need to generate recommendations instantly every time a user interacts with the platform. Instead, the retailer might collect user interaction data over the course of a day and then run the recommendation algorithm overnight. The next day, when users log in, they receive personalised recommendations based on the latest batch of predictions.
Batch Inference Workflow
- A large batch of data is collected.
- The entire dataset is fed to the model.
- Predictions are generated and typically stored in a database or file.
- Results are processed or made available for downstream tasks.
Advantages and Disadvantages of Batch Inference
First, let’s start with the advantages of batch inference. Batch processing is highly efficient for handling large volumes of data. Since the system processes data in bulk, it allows an optimisation for high throughput, making it an ideal choice for large-scale tasks. Furthermore, Resource Management: Predictions generate in bulk, hence resource management is more effective. High computational resources are used only during the scheduled batch processing, rather than continuously. And finally, batch inference is easier to manage when predictions are not time-sensitive. You can schedule tasks at times of low system usage, such as during off-peak hours, to minimise the impact on other operations.
On the other hand, batch inference comes with its own disadvantages. One of the main drawbacks of batch inference is the delay in obtaining results (latency). Predictions are only made when a batch is processed, meaning that new data must wait until the next batch run to be included in the predictions. Furthermore, while batch processing can handle large datasets, it requires sufficient computational resources to process these large batches. As data volumes grow, the demands on the system increase, potentially leading to scalability challenges.
What is Online Inference?
Online inference (also dynamic or real-time inference) involves making predictions in real-time, typically one at a time or in small groups, as data becomes available. This approach is crucial when predictions are immediately necessary, such as in real-time decision-making systems.
A prime example of online inference is real-time fraud detection in financial transactions. As transactions are processed, they need to be evaluated instantaneously to determine whether they are fraudulent. Delays in this process could result in financial loss or security breaches. Online inference allows the system to make these decisions in real-time, based on the most current data.
Online Inference Workflow
- Data is received from a user interaction or system event.
- The data is processed and fed to the model.
- The model generates a prediction in real-time.
- The prediction is immediately used for decision-making.
Advantages and Disadvantages of Online Inference
Again, we first start with the advantages of online inference. The most significant advantage of online inference is its ability to provide immediate predictions. This is essential for applications that require real-time decision-making, such as autonomous vehicles or live customer support systems. Furthermore, online inference can quickly adapt to changes in data patterns. Since predictions are continuous, the model can update more frequently, ensuring that it remains accurate and relevant. And finally, in applications where user interaction drives data, such as personalised marketing, online inference allows for dynamic adjustments based on user behavior, improving engagement and user experience.
On the contrary, online inference also carries some disadvantages. First, online inference can be computationally expensive, especially if predictions are very frequent. The system must be capable of handling continuous data streams, which requires a robust and scalable infrastructure. Also, implementing online inference requires a more complex system architecture compared to batch processing. Ensuring low latency, high availability, and fault tolerance in real-time systems can be challenging and resource-intensive. And finally, the need for continuous operation and updates in real-time systems can lead to higher maintenance costs and efforts. This is in comparison to batch systems, which allows updating and management at scheduled intervals.
Choosing the Right Approach
When deciding between batch and online inference, the primary consideration should be the specific needs of your application. Batch inference is ideal for scenarios where predictions are not in need immediately and where it is possible to process data in large chunks. This approach is common in analytics, report generation, and systems where data accumulates over time.
On the other hand, online inference is necessary for applications that require real-time predictions. This approach is essential in environments where decisions need to happen instantaneously, such as in financial trading, autonomous driving, and real-time personalisation.
Both batch inference and online inference are powerful tools in machine learning, each with its own strengths and weaknesses. The choice between the two depends on the specific requirements of the task at hand, particularly the need for immediacy in predictions. Below is a table outlining the key differences between batch and online inference.
| Aspect | Batch Inference | Online Inference |
|---|---|---|
| Timing | Scheduled, periodic | Real-time, as data arrives |
| Data Processing | Large datasets at once | Single or small batch of data at a time |
| Use Case | Non-time-sensitive tasks (e.g., report generation) | Time-sensitive tasks (e.g., fraud detection) |
| Performance | Can be optimised for throughput | Optimised for low latency |
| Resource Usage | High resource usage during batch processing | Constant resource usage, optimised for quick response |
| Complexity | Easier to manage and scale | Requires more complex infrastructure |
