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victorNguyen- Instructor Rating - Reviews - Students - CoursesNo biography available, please complete
📘 About This Course
This is a build-it course for product people who are tired of AI slideware.
Across ten modules and 64 lessons you take one AI product — a tier-one support triage assistant that drafts a first reply from a ticket and a screenshot — from a vague idea to a launch decision you can defend line by line. Everything happens in a real Ubuntu lab over SSH, against a live multimodal LLM REST API. Nothing is simulated: you see the real request, the real HTTP response, the real pass rate and the real failure.
You start by discovering the API contract instead of assuming it, then frame the problem, design the human-AI loop, and build a working prototype that calls the model with your own JSON schema. From there you instrument it, define a north-star metric, build an evaluation set out of real product scenarios, and run experiments that separate model variance from product impact.
You make it safe — prompt injection, privacy, bias, incident drill — then price it, package it, and take it through a launch gate with nine mandatory criteria and seven named sign-offs.
Every module hands one artifact down to the capstone: the evidence log, the acceptance criteria, the working prototype, the metric definitions, the risk register, the roadmap and the go-to-market plan. What you finish with is not a certificate of attendance. It is an AI product launch package you built yourself.
Every lesson ships the commands and sources actually executed on screen, so you can reproduce the whole thing in your own lab.
What You'll Learn
Build a working multimodal AI product end to end - from the API contract to a prototype with schema validation, timeouts, retries and product events.
Decide with evidence: north-star metrics, an offline evaluation set, A/B tests, and a decision dashboard that answers whether the product is actually useful.
Design the human-AI loop - confidence, review, escalation and honest refusal - so the product never invents an answer or sends one without a human.
Make it launchable: risk mapping, privacy, prompt-injection mitigations, unit economics, positioning, and a launch gate with named accountability.
🛠️ Requirements
- Comfort with a Linux terminal and basic Python - you will use SSH, bash, curl, jq and a small Python client. No machine-learning background is required.
- An environment where you can run a Linux shell and reach an LLM REST API endpoint. Every install step is shown and every command is provided, so the lab is reproducible.
👤 Who This Course is For
- Product managers, product owners and technical founders who have to ship an AI feature and defend the decision with evidence rather than demos.
- Engineers, designers, data and analytics people who sit next to AI product decisions and want the metrics and governance artifacts that make them stick.
- Anyone who has built an AI demo and now has to turn it into a product that can be measured, priced and launched.
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John Doe
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet