Conclusion

Tell the whole story

A team collects examples for a problem. It selects a learning method, trains a model, and checks it on suitable separate data. An application then uses that model to produce predictions or content. People monitor the system because the world and its inputs can change.

The model is one component. Its data, connected software, and decisions also matter. Revisit Google’s production ML overview to see that broader system.

A short memory card

Term	A plain-English reminder
Model	The learned system used to produce an output
Feature	Information supplied as an input
Label	A target answer attached to an example
Parameter	A model value adjusted during training
Training	Learning or adjusting the model
Inference	Using the model
Loss	A measure of mismatch with a training target
Token	A small unit the language model processes
Context	Information available for the current response

Keep these distinctions clear

• Deep learning can use labelled data.
• A mathematical neuron is not a copied brain cell.
• A token is not always a whole word.
• A prompt normally changes context, not learned parameters.
• A model’s confidence does not replace independent evidence.

Practise explaining, not memorising

A colleague says, “The model learned this fact because I typed it into the chat.” What would you explain?

The model may use that fact while it is available in the conversation. That is different from a training process that changes its parameters. Remembering something in the current context does not prove a permanent model update.

Next connection: security threats

You can now ask where an attacker might interfere: the examples used to learn, the inputs used at prediction time, the model files, or the application acting on its output. These questions connect the building blocks to the next room, AI Security Threats.