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The AI Stack — A Mental Model

This lesson breaks down the AI stack into four layers: data, model, infrastructure, and application. Each layer is essential for building effective AI products, and understanding them helps PMs make better decisions and improve their products.

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The AI Stack — A Mental Model

Welcome, product managers! Today, we're diving into the AI stack. Think of it like the layers of a delicious cake—each layer is essential for the final product. Only here, the layers are data, model, infrastructure, and application. We'll break these down and explain why each matters for you.

Why This Matters

As a PM, understanding the AI stack means you can navigate discussions with engineering, evaluate vendors intelligently, and make informed decisions about your product's AI capabilities. No more nodding along pretending to understand what 'scalable architecture' means!

The Layers of the AI Stack

1. Data Layer

This is the foundation of your AI cake. Without quality data, your AI is like a car without fuel—immobile and useless.

  • Data Collection: Gathering relevant data. Think Google collecting search queries.
  • Data Cleaning: Removing noise and inconsistencies. Like Marie Kondo-ing your dataset.
  • Data Annotation: Adding labels to data. Imagine tagging your photos with 'cat' or 'dog'.

What comes next

Why This Matters: As a PM, you need to ensure data quality. Garbage in, garbage out. Poor data means poor AI performance.

2. Model Layer

Here’s where the magic happens. Models are algorithms trained on data to make predictions or decisions.

  • Model Selection: Choosing the right model for the task. It's like picking the right tool for the job.
  • Training: Feeding data into the model to teach it.
  • Validation: Checking the model's accuracy.

Why This Matters: Understanding this helps you set realistic expectations for what AI can achieve in your product.

3. Infrastructure Layer

This is the engine room that keeps everything running smoothly.

  • Computing Power: Cloud services like AWS or GCP that provide the horsepower for AI tasks.
  • Data Storage: Where all your data is kept, like a digital library.
  • Deployment: Getting your model into the hands of users.
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