AI inference service refers to the process of using trained AI models to make predictions or decisions on input data. In the inference phase, the model no longer needs to learn new knowledge but focuses on using existing knowledge to solve practical problems. For example, when you upload an image, the AI inference service can recognize objects or faces in the image; when you input speech, it can convert it to text; when you input text, it can analyze its sentiment or generate replies. The core goal of AI inference services is to efficiently and quickly apply models to practical scenarios while ensuring the accuracy and stability of results.

The difference between AI inference service and AI training is mainly reflected in the following aspects:

Different purposes: AI inference service aims to use trained models to make predictions or decisions on input data, while AI training is to train new models through large amounts of data and computational resources.

Data dependency: Inference services rely on pre-trained models, while training requires large amounts of data and computational resources.

Application scenarios: Inference services are typically used to process existing data, while training is used to generate new models.

Cost: Inference services typically cost less than training because they don't require as much computational resources.

Latency: Latency refers to the time it takes from submitting input data to the AI inference service to receiving the result. For example, when a user uploads an image for recognition, the time from upload completion to receiving the recognition result is the latency. Latency is typically measured in milliseconds (ms), and low latency is a key requirement for many real-time applications (such as autonomous driving, voice assistants).

Throughput: Throughput refers to the number of requests an AI inference service can process per unit time. For example, if a service can process 100 image recognition tasks per second, its throughput is 100 QPS (Queries Per Second). High throughput is suitable for scenarios that need to process a large number of requests, such as recommendation systems or batch data processing.

Relationship between latency and throughput: Typically, reducing latency may sacrifice some throughput, and vice versa. Therefore, when designing AI inference services, it's necessary to balance these two requirements based on specific scenarios.

Data encryption: Use encryption technologies (such as SSL/TLS protocols) during data transmission and storage to ensure data is not stolen or tampered with.

Model protection: Prevent models from being maliciously copied or reverse-engineered. Models can be protected through model encryption, obfuscation, or using dedicated hardware (such as Trusted Execution Environment TEE).

Access control: Limit access to AI inference services to only authorized users or systems through authentication (such as API keys, OAuth) and permission management.

Input and output validation: Check the validity of input data to prevent malicious inputs (such as adversarial sample attacks) from causing the model to output incorrect results. At the same time, filter output results to avoid leaking sensitive information.

Logging and monitoring: Record service operation logs, monitor abnormal behaviors in real-time (such as high-frequency requests, abnormal inputs), and promptly discover and respond to potential security threats.

Privacy protection: For data involving user privacy (such as medical images, personal identity information), federated learning or differential privacy techniques can be used to ensure data is not leaked during the inference process.