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Production AI Architecture on Android

Design production Android AI systems with protected inference, offline behavior, evaluation, observability, privacy and controlled rollout.

Intermediate to AdvancedSelf-paced7 modules13 available lessons

Your path, one skill at a time.

Design production Android AI systems with protected inference, offline behavior, evaluation, observability, privacy and controlled rollout. Follow six teaching modules, each with a practical assignment, then combine the skills in the final project. Start with the introduction even if the tools are familiar: it defines the scope and boundaries of the path.

Self-paced. Complete each exercise and use its checkpoint before moving to the next module. Revisit any prerequisite you cannot yet demonstrate.

What you’ll learn

  • Document trust and data-flow boundaries
  • Demonstrate rate limits, timeout recovery and tenant isolation
  • Run a held-out evaluation and exercise the feature kill switch

THE CURRICULUM

Build your knowledge, step by step.

13 lessons across 7 modules. Open a module to explore its lessons and practical work.

01MODULE 01What production readiness means2 lessons · Self-paced
  • Quality requirements
  • Operational requirements
  • Architecture boundaries

Translate a working demo into explicit service and product requirements.

  1. 01What production readiness meansLesson · Self-paced
  2. 02Practice: What production readiness meansLab · Self-paced

Ready to move on when: Every architecture choice can be traced to a product or operational requirement.

02MODULE 02Security, identity and data isolation2 lessons · Self-paced
  • Credential placement
  • Authorization per operation
  • Prompt injection

Protect the application around the model.

  1. 01Security, identity and data isolationLesson · Self-paced
  2. 02Practice: Security, identity and data isolationLab · Self-paced

Ready to move on when: An adversarial prompt cannot expand access beyond the authenticated user’s permissions.

03MODULE 03Reliability, offline behavior and retries2 lessons · Self-paced
  • Failure classification
  • Idempotent operations
  • Offline contracts

Design useful outcomes when dependencies fail.

  1. 01Reliability, offline behavior and retriesLesson · Self-paced
  2. 02Practice: Reliability, offline behavior and retriesLab · Self-paced

Ready to move on when: Every retry policy is bounded and accounts for possible prior completion.

04MODULE 04Evaluation and regression gates2 lessons · Self-paced
  • Representative datasets
  • Task-level scoring
  • Change attribution

Make quality changes visible before rollout.

  1. 01Evaluation and regression gatesLesson · Self-paced
  2. 02Practice: Evaluation and regression gatesLab · Self-paced

Ready to move on when: Your release gate can reject an attractive demo that fails important edge cases.

05MODULE 05Observability, budgets and privacy2 lessons · Self-paced
  • Correlation and redaction
  • Cost per outcome
  • Retention and deletion

Collect enough evidence to operate the feature without over-collecting user data.

  1. 01Observability, budgets and privacyLesson · Self-paced
  2. 02Practice: Observability, budgets and privacyLab · Self-paced

Ready to move on when: You can diagnose a failed task without routinely storing its full sensitive payload.

06MODULE 06Rollout, rollback and incident response2 lessons · Self-paced
  • Feature controls
  • Rollback scope
  • Incident playbook

Make the release reversible and the operating plan concrete.

  1. 01Rollout, rollback and incident responseLesson · Self-paced
  2. 02Practice: Rollout, rollback and incident responseLab · Self-paced

Ready to move on when: Another engineer can disable or roll back the feature from your documented procedure.

07MODULE 07Capstone and portfolio review1 lessons · Self-paced
  • Document trust and data-flow boundaries
  • Demonstrate rate limits, timeout recovery and tenant isolation
  • Run a held-out evaluation and exercise the feature kill switch

Harden an AI document assistant with authenticated access, bounded inference, evaluation gates, telemetry and a tested rollback.

  1. 01Capstone: Production AI Architecture on AndroidProject · Self-paced

Ready to move on when: Demonstrate every acceptance criterion and explain the tradeoffs without relying on the lesson text.

PROVE YOUR SKILLS

A portfolio you can build, explain, and defend.

  • Document trust and data-flow boundaries
  • Demonstrate rate limits, timeout recovery and tenant isolation
  • Run a held-out evaluation and exercise the feature kill switch

Use the final review rubric to identify gaps. Lesson completion tracks study progress; readiness comes from independently demonstrating the work.

Open the portfolio review rubric →