Season 5: The AI Proposal · Episode 1 of 8
Do We Need AI for That?
You play: AI Engineering Intern
Included with Core programme and Certification prep
The situation
Leo has circulated a product doc titled 'AI everywhere': an AI button on every screen — auto-generated creator bios, AI-suggested thumbnails, an AI 'vibe score', AI moderation. Amara's response is one line: 'Which of these is a user problem that actually needs a model?' She wants you to sort the list, and to show — with numbers — where a model beats the rules Code Nexus already runs.
What you'll learn
- AI-901 / AIF concepts and machine-learning foundations: supervised vs unsupervised learning, features and labels, classification / regression / clustering, the model lifecycle, training and inference, train/test splits, and deciding when a model is the right tool versus rules.
- The six responsible AI principles (fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability) and how to tell them apart in a scenario, and the AI workload families: computer vision, natural language processing, speech, information extraction and generative AI.
Who you work with
Amara Okafor
ML and Data Engineer
Leo Martins
Product Manager
8-BIT
Code Nexus Internal AI Assistant
Scenes
- 1.AI everywhere
- 2.What kind of problem is it
- 3.Training and inference
- 4.Rules versus a model, on the numbers
- 5.Which principle is at stake?
- 6.The use-case brief
- 7.Concepts check
What you leave with
You turned the AI-everywhere document into an evidence plan. In the fictional fixture, the classifier scored 0.875 against a 0.62 rules baseline. Captions and moderation triage proceed for further evaluation; auto-bios stay with a template until their problem and criteria are supported.