What is AI inference?

While the complete AI life cycle involves everything from data collection to long-term monitoring, a model's central journey from creation to execution has three key stages. The first two are about learning, while the last one is about putting that learning to work.

• AI training is the foundational learning phase. It's a computationally intensive process where a model analyzes a massive dataset to learn patterns and relationships. The goal is to create an accurate and knowledgeable model. This requires powerful hardware accelerators (like GPUs and TPUs) and can take anywhere from hours to weeks.
• AI fine-tuning is a shortcut to training. It takes a powerful, pre-trained model and adapts it to a more specific task using a smaller, specialized dataset. This saves significant time and resources compared to training a model from scratch.
• AI inference is the execution phase. It uses the trained and fine-tuned model to make fast predictions on new, "unseen" data. Each individual prediction is far less computationally demanding than training, but delivering millions of predictions in real-time requires a highly optimized and scalable infrastructure.
• AI serving is the process of deploying and managing the model for inference. This often involves packaging the model, setting up an API endpoint, and managing the infrastructure to handle requests.

This table summarizes the key differences:

At its core, AI inference involves three steps that turn new data into a useful output. 

Let's walk through it with a simple example: an AI model built to identify objects in photos.

1. Input data preparation: First, new data is provided — for instance, a photo you've just submitted. This photo is instantly prepped for the model, which might mean simply resizing it to the exact dimensions it was trained on.
2. Model execution: Next, the AI model analyzes the prepared photo. It looks for patterns — like colors, shapes, and textures — that match what it learned during its training. This quick analysis is called a "forward pass," a read-only step where the model applies its knowledge without learning anything new.
3. Output generation: The model produces an actionable result. For the photo analysis, this might be a probability score (such as a 95% chance the image contains a "dog"). This output is then sent to the application and displayed to the user.

While a single inference is quick, serving millions of users in real time adds to the latency, cost, and requires optimized hardware. AI specialized Graphics Processing Units (GPUs) and Google's Tensor Processing Units are designed to handle these tasks efficiently along with orchestration with Google Kubernetes Engine, helping to increase throughput and lower latency.

This is the most common approach, where inference runs on powerful remote servers in a data center. The cloud offers immense scalability and computational resources, making it ideal for handling massive datasets and complex models. Within the cloud, there are typically two primary modes of inference:

• Real-time (online) inference: Processes individual requests instantly as they arrive, often within milliseconds. This is crucial for interactive applications that demand immediate feedback.
• Batch (offline) inference: Handles large volumes of data all at once, typically when immediate responses aren't required. It's a highly cost-effective method for periodic analyses or scheduled tasks.

This approach performs inference directly on the device where data is generated — this could be on a smartphone, or an industrial sensor. By avoiding a round-trip to the cloud, edge inference offers unique advantages:

• Reduced latency: Responses are nearly instantaneous, critical for applications like autonomous vehicles or real-time manufacturing checks.
• Enhanced privacy: Sensitive data (such as medical scans, personal photos, video feeds) can be processed on-device without ever being sent to the cloud.
• Lower bandwidth costs: Processing data locally significantly reduces the amount of data that needs to be uploaded and downloaded.
• Offline functionality: The application can continue to work even without an internet connection, ensuring continuous operation in remote or disconnected environments.

To help you choose the best approach for your specific needs, here’s a quick comparison of the key characteristics and use cases for each type of AI inference:

AI inference presents a distinct set of technical challenges, including managing latency, controlling costs, and ensuring scalability. Google Cloud provides a flexible path for inference, allowing you to choose the right tools based on your model's complexity, performance needs, and operational capacity. You can start with fully managed solutions and progressively adopt more customized infrastructure as your requirements evolve.

This approach is ideal for developers of any skill level, including those new to AI, who want to integrate powerful AI capabilities quickly. It requires making simple API calls without needing to manage any models or infrastructure.

This option is for developers who already have a custom model built. You can deploy it to Google Cloud’s managed service, which means you don't have to handle the complex server setup or orchestration yourself. You get to focus on your model, not the infrastructure.

Gives developers and MLOps granular control and flexibility to deploy, manage, and scale custom containerized inference services, often with specialized hardware, across cloud or hybrid environments.

If you work with SQL, you can now get predictions from AI models right where your data already lives. This eliminates the need to move data to a separate platform, simplifying your workflow.