AI Product Management: Defining and Delivering AI-Powered Features
- Hands-on AI and data science techniques from working practitioners
- Structured program with clear milestones and real-world datasets
- Accessible remotely — join from anywhere in Ukraine
About this program
The product side of AI work
Building an AI feature is different from building a standard software feature. Requirements are probabilistic, timelines are harder to estimate, and failure modes are less predictable. Product managers who understand these differences make better decisions for their teams.
This program does not teach you to train models. It teaches you to work effectively with the people who do — and to ask the questions that prevent wasted engineering time.
Core topics
- Scoping AI problems: when ML is the right tool and when it is not
- Data requirements: what data you need before a model can be built
- Evaluation metrics: how to define success for a model in business terms
- Ethics and risk: bias, fairness, and regulatory considerations
- Roadmap planning: managing uncertainty in AI project timelines
Format
Six weeks, one live session per week, plus reading assignments and case study discussions. Sessions are recorded. Participants work through four real product scenarios drawn from healthcare, e-commerce, and logistics.
Program structure
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Week 1 — AI Capabilities and Limitations
What current AI systems can and cannot do reliably. Common failure modes in production.
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Week 2 — Problem Framing
Translating business problems into ML problem statements. Defining the right objective function.
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Week 3 — Data Strategy
Data collection, labeling, and quality requirements. Working with data teams on feasibility.
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Week 4 — Metrics and Evaluation
Connecting model metrics to business KPIs. Setting acceptable performance thresholds.
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Week 5 — Ethics, Bias, and Compliance
Identifying fairness risks, EU AI Act basics, documentation requirements.
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Week 6 — Roadmap and Delivery
Estimating AI project timelines, managing stakeholder expectations, post-launch monitoring.