What is Model Serving

What is Model Serving?

Model serving is a process that makes AI models available for use in real-world applications. Think of an AI model like a highly trained expert who can make predictions or decisions based on the data they’ve been trained on. But, just like how an expert needs a platform to share their knowledge, an AI model needs a way to be shared and used by others. Model serving is that platform. It’s a system that takes the trained AI model and makes it accessible to users, either through a website, an app, or other software. This way, the model can be used to make predictions, classify data, or generate text, and its results can be easily accessed and used by others. The goal of model serving is to make it easy to deploy and manage AI models, so they can be used to solve real-world problems.

Think of It Like This

Imagine you’ve hired a team of experts to help you make decisions about your business. Each expert has their own specialty, and you need to be able to ask them questions and get their advice. Model serving is like a receptionist who connects you with the right expert at the right time. You ask your question, and the receptionist directs it to the right expert, who then provides their answer. In the same way, model serving directs requests to the right AI model, which then provides its prediction or decision. This analogy isn’t perfect, but it gives you an idea of how model serving acts as a bridge between the user and the AI model.

Why Should You Care?

Model serving matters because it affects how we use AI in our daily lives. For example, when you ask a virtual assistant like Siri or Alexa to play your favorite song, model serving is what makes it possible for the assistant to understand your request and respond accordingly. It’s also what allows online retailers to recommend products based on your browsing history, or what enables social media platforms to suggest friends you might know. Without model serving, these applications wouldn’t be able to use AI models to make predictions and decisions. As AI becomes more ubiquitous, model serving will play an increasingly important role in making sure that AI models are deployed effectively and efficiently.

Where You’ve Already Seen It

You’ve probably already seen model serving in action, even if you didn’t realize it. For example, ChatGPT uses model serving to provide its text generation capabilities. When you ask ChatGPT a question, it uses a complex AI model to generate a response, and model serving is what makes that response available to you. Another example is Netflix, which uses model serving to provide personalized movie recommendations. Netflix trains AI models on user data, and then uses model serving to deploy those models and generate recommendations for each user. Spotify is another example, using model serving to provide personalized music recommendations based on user listening habits. In each of these cases, model serving is what makes it possible for the AI model to be used in a real-world application.

The One Thing to Remember

The key thing to remember about model serving is that it’s what makes AI models usable in the real world. Without model serving, AI models would just be complex algorithms sitting on a computer somewhere, unable to be used to solve real-world problems. Model serving is what bridges the gap between the AI model and the user, making it possible for the model to be deployed and used effectively. This is an important concept to understand, because it’s what makes AI so powerful and useful in a wide range of applications.

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