Deploying Models
A model on your laptop helps nobody else. Deploying means putting it online behind an API, so any app can use it. This lesson shows the two paths, the cost and waiting time, and what to check before you launch.
New words
- Deploy
- Put your app or model on a server so other people can use it.
- Latency
- The waiting time before the answer starts or ends.
- Streaming
- Sending the answer word by word, as it is written.
- Rate limit
- A cap on how many requests one user can send in a given time.
- Container
- A sealed package with your app and everything it needs to run.
- Fallback
- A backup plan that works when the main model fails.
- GPU
- A strong chip that runs large models fast. It is costly to rent.
What you will learn
- A model on your laptop helps nobody
- Two paths: hosted or your own
- The request path and your API key
- Latency, cost and streaming
- Containers, scaling and caching
- Try it: plan your deployment
- Trade-offs
- In a real system: the pre-launch checklist
- Interview tip
- Key takeaways
10 quiz questions at the end.
The full lesson, with interactive demos and the quiz, opens after you sign in with Google. It is free.