Run AI applications in production: build evaluation suites, control cost and latency, trace and monitor, defend against injection and data leaks, choose between prompting, RAG and fine-tuning, and ship a monitored app.
Level
04
Modules
05
Lessons
22
Introduction
Building an Evaluation Dataset
Downloadable Assets and Resources
Metrics and Rubrics
Model Judges Done Right
Regression Testing Prompts and Pipelines
Online Feedback and Experiments
Assessment
The demo works, so how do you know it is good enough to ship, and how do you keep it that way? This course is about the part of AI engineering that comes after the first prototype: measuring quality, making the system fast, cheap and reliable, securing it and choosing the right way to adapt a model. It is the most practical of the AI Engineering courses, and it ends with an AI application that you evaluate, harden, trace and ship.
The course uses a simulated support assistant with three versions (v1 to v3), so that you can measure a real improvement and a real regression, and all printed results are reproducible without an API key. You work with an evaluation set, a set of attack prompts, a judge stand-in with labeled examples for calibrating a judge, sample traces and a fine-tuning data file. The lessons on Docker, cloud deployment and the GPU fine-tuning code show the commands and code but were not run for the printed output; each lesson says which is which.
Every lesson starts from an operational problem, explains the principle with the definitions and the trade-offs, shows code with real output, and finishes with exercises and a summary.
Shipped and Monitored AI App: you write the evaluation and the regression tests, harden the app, add tracing and cost control, then deploy, monitor and write the report. A template repository and a worked solution repository are included (with 28 tests, a FastAPI service and a Dockerfile).
Lessons on evaluation, reliability, cost and speed, security and governance, and choosing and adapting models, datasets for evaluation, attacks, judging and fine-tuning, a simulated assistant to measure, a guided shipped and monitored app project with template and solution repositories, and a quiz for every module.
Building the demo is the easy part. Shipping something you can measure, secure, afford and keep improving is what employers and customers pay for, and this course teaches exactly that. If you have built a prototype and want to take it to production with confidence, enroll now and finish the AI Engineering path with the skills that make an AI application last.
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