Monitoring, Logging and Versioning AI Applications

Introduction to Monitoring, Logging and Versioning AI Applications

In the previous lesson, we focused on making AI applications more efficient by reducing unnecessary token usage, choosing the right model, and avoiding repeated work.

Now we are going to focus on what happens after an AI application is running.

We can now observe what the application is doing, detect problems, and connect changes in behavior to specific versions of our prompts, models, configuration, and code.

In the next lesson, we will bring everything together by looking at Deploying AI Applications using platforms such as Streamlit Cloud, Hugging Face Spaces, and Google Cloud Run.