Managing AI Product Development

What you will be able to do

• Map any AI initiative onto the full product lifecycle and name the risks that live at each stage
• Judge whether a problem is a genuine fit for machine learning before a team writes any code
• Define layered success metrics that tie model performance to product and business outcomes
• Build a data strategy covering sourcing, labeling, quality, and governance
• Run feasibility spikes and experiments with data science and engineering without overpromising the roadmap
• Read a model evaluation and explain precision, recall, and error trade-offs in plain language
• Prototype an AI feature with Wizard of Oz and off-the-shelf models before funding a custom build
• Design AI experiences that set expectations, handle low confidence, and recover when the model is wrong

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   The AI Product Lifecycle

   AI products do not behave like ordinary software. They learn from data, return probabilities instead of fixed answers, and drift after they launch. This module maps the full lifecycle, from problem discovery through data, modeling, evaluation, deployment, and monitoring, and marks exactly where the product manager owns the outcome. By the end you can tell which problems genuinely need machine learning and which are better and more cheaply solved with plain rules.

   2 lessons · 5 quiz questions

2. 02

   Success Metrics and Data Strategy

   An AI product is only as good as the outcome you measure it against and the data it learns from. This module teaches you to define success across model, product, and business metrics, tell a metric that moves the business from a vanity number, and build a data strategy covering sourcing, labeling, quality, and governance so data gaps surface before they sink a launch.

   2 lessons · 5 quiz questions · assignment

3. 03

   Working with Data Science and Engineering

   AI gets built by data scientists, ML engineers, and platform teams whose work is research, not a standard sprint. This module teaches you to scope feasibility spikes, frame experiments that yield trustworthy results, read model evaluation metrics, and manage the handoff from notebook to production, so you can translate between the model and the roadmap without pretending to be a data scientist.

   2 lessons · 5 quiz questions

4. 04

   Prototyping and AI User Experience

   De-risk an AI feature before the model exists. This module teaches rapid prototyping (Wizard of Oz, off-the-shelf models, and prompt mockups) and the design patterns that keep an AI experience useful and trustworthy even when the model gets it wrong.

   2 lessons · 5 quiz questions · assignment