Srileo LearnProfessional Learning Platform

BUILDING AND SHIPPING AI PRODUCTS

AI Product Management & Global Infrastructure

The builder's counterpart to Practical AI for Future Managers. That course is written for the organisation deploying AI internally; this one is for the team whose product is the AI system, and who therefore inherit the infrastructure, latency, residency and dependency questions that come with shipping it to real users in more than one country.

  • 8 modules
  • 24 lessons
  • 9 quizzes
  • 105 questions
  • About 12 hours

Free — no enrolment needed

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Course syllabus

Eight modules in two halves. Part one is the product discipline: what makes an AI product different, designing for probabilistic systems, evaluation, and economics. Part two is the substrate: where AI runs, data residency and sovereignty, going global, and resilience and sustainability. Each module ends with a quiz, and a final assessment covers all eight. Every lesson and every quiz is free.

  1. 1. What Makes an AI Product Different

    Why non-determinism changes the product discipline rather than just the engineering, what the AI product manager's job actually contains, and what has to be validated before building something whose behaviour you cannot fully specify.

  2. 2. Designing for Probabilistic Systems

    The interface patterns that make uncertain output usable, how to design the failure paths that are a real part of the product, and how disclosure and trust get built into the surface rather than added to the terms.

  3. 3. Evaluation as a Product Discipline

    Building the evaluation suite that functions as your specification, combining offline measurement with live signal and human judgement, and shipping safely when the model underneath your product can change without you changing anything.

  4. 4. The Economics of an AI Product

    Why AI products break the software margin model, how the capability ladder from prompting to training trades cost against control, and how to price something whose cost of goods rises with every use.

  5. 5. Where AI Actually Runs

    The physical substrate: what training and inference each demand, how accelerators and data centres shape what is available to you, why the speed of light sets a floor on latency you cannot engineer past, and what capacity constraints mean for a product roadmap.

  6. 6. Data Residency and Sovereignty

    Where data may legally live and move, why the legal basis for transatlantic transfer has been unstable for a decade, what sovereign AI means for architecture, and how to build a product that can be sold in more than one jurisdiction.

  7. 7. Going Global

    Why model quality is not uniform across languages, what running in several regions actually costs operationally, and the market-by-market constraints — connectivity, devices, payments, price sensitivity — that decide whether a product works outside the market it was designed in.

  8. 8. Resilience, Dependency and Sustainability

    The concentration risk that comes from building on a handful of providers, the reliability engineering an AI product specifically needs, and the energy and environmental constraints that are becoming a genuine planning input rather than a corporate responsibility footnote.

Source and attribution

Independently written by Srileo Technologies, and vendor-neutral. Infrastructure and regulatory figures are attributed in the lessons to their published sources — principally the IEA's Energy and AI analysis, published multilingual benchmark research, and the public record of the EU–US Data Privacy Framework litigation. Capacity, pricing and sovereign-compute figures move quickly; where a number is volatile or its source is weak, the lesson says so and teaches the calculation instead of the number.