[Non-Technical] AI Product Manager Explorer Certificate

Explore common ai patterns such as classification, regression, ranking, generative ai, and detection and matching. Learn to define categories, set accuracy thresholds, and run experimentation as a product manager.

Explore how domain-specific ai drives healthcare, finance, retail, manufacturing, media, and education, and learn to design with regulatory compliance, ethics, and user trust at the forefront.

Identify AI-ready problems by evaluating feasibility, data alignment, and impact; ensure high-volume, data-rich, and feedback-enabled opportunities that deliver clear user value.

Explore how AI shapes user impact and value by improving experience, trust, and perceived usefulness, while balancing control and collaboration with the user.

Frame AI opportunities by aligning business goals, user needs, and data feasibility to guide product development. PMs translate ambitions into clear problems and communicate value to data teams and users.

Navigate the AI lifecycle as a loop from defining the right problem to deployment. As a product manager, align stakeholders and ensure data collection, modeling, evaluation, and ongoing learning.

Lead cross-functional ai product teams, including data scientists, ml engineers, ml ops, data engineers, and annotators, with the product manager aligning goals, deployment, and ethical product evolution.

Position data as a strategic asset and foundation for AI-powered products, ensuring completeness, accuracy, timeliness, and representativeness to drive successful outcomes.

Iterate with AI product development by embracing a learning-driven cycle: gather data, run experiments, ship an MVP, and continuously retrain as the model drifts.

Map a case exercise end-to-end AI workflow from problem framing to deployment and iteration, analyzing data sources, modeling choices, stakeholders, and feedback loops to optimize AI product outcomes.

Frame AI opportunities with the three legged stool: problem, solution, and data. Ensure real user needs, appropriate AI, and data readiness before proceeding.

Bridge the gap between user needs and data availability to make AI features viable, using data-first strategies, measurable signals, and transparent tradeoffs.

Define AI goals and success criteria that align model performance with business value, linking metrics like engagement, retention, time to resolution, cost per ticket, and upsell conversion to real outcomes.

Align product vision with AI capabilities by scoping the smallest viable version, using a versioned roadmap from v0 to v2, and communicating uncertainty to stakeholders.

Frame AI product use cases through analytical, data-backed framing and guardrails. Avoid AI-first thinking by asking practical questions about user problems, data, and measurable success.

Coordinate data labeling, modeling, and deployment among data scientists, ML engineers, data engineers, and annotators, aligning work with the product vision and success criteria.

Learn to run agile ai sprints by separating discovery and experimentation from delivery, planning around uncertainty, and aligning learning goals with product value.

Coordinate cross-functional teams—design, engineering, legal, and operations—to align AI product vision, ensure explainability, address bias and governance, and maintain stable deployment through shared rituals and tools.

Learn how to lead AI product teams through uncertainty by setting expectations, framing risk, and communicating progress with scenarios, demos, and resilience.

Navigate conflicts and trade-offs in AI teams by clarifying priorities and articulating consequences for model performance. Use decision matrices to balance user needs, ethics, and business goals.

Understand core ai metrics—accuracy, precision, recall, and f1—and how they translate to real product impact. Learn to balance trade-offs and explain the metrics to your team.

Discover how human in the loop and feedback improve AI safety, trust, and performance by coordinating labeling, feedback loops, and cross-team collaboration across product teams.

Assess and mitigate bias, opacity, and unfair outcomes in ai systems by embedding ethical goals, explainability, and diverse perspectives into product design and evaluation.

Define success beyond the model by evaluating AI features with business KPIs, user outcomes, and real-world impact, not just accuracy; track conversion, churn, retention, and user trust to drive value.

Translate complex ai metrics into clear, actionable updates for stakeholders using dashboards, visualizations, and status signals, with thresholds, confidence bands, and audience-specific views.

Identify, evaluate, and select AI use cases by balancing user needs, data availability, feasibility, and impact, then validate with users and data to prioritize 1-2 strong candidates.

Design a human-in-the-loop feedback system to guide AI decisions, collect actionable user input, and iteratively improve model performance, safety, and trust.

Outline the roadmap and metrics for AI products, translating vision into phased timelines, risk-aware milestones, data exploration, experiments, and cross-functional collaboration from discovery to delivery.

Explain your AI product plan in plain language with clear framing, trade-offs, and ROI, connect features to business outcomes, and outline next steps to secure cross-functional buy-in.

Learn to conduct peer review and finalize your AI product plan for final certification by presenting use cases, defining metrics, mapping roadmaps, and integrating feedback.