Disclaimer
Contributors
This course was designed and is maintained by:
| Name | |
|---|---|
| Humberto Sandmann | |
| Fabio Roberto de Miranda | |
| Raul Ikeda | |
| Maciel Calebe Vidal | |
| Eduardo Felipe Zambom Santana |
AI-Assisted Content
This course material was partially co-authored with AI assistance.
Portions of this documentation — including class notes, quiz questions, lab instructions, code examples, and diagrams — were drafted with the help of Claude (Anthropic). All AI-generated content was reviewed, edited, and validated by the course instructor before publication.
What AI was used for
| Content type | AI role |
|---|---|
| Class notes and explanations | First-draft generation, then instructor review and revision |
| Mermaid diagrams | Structural suggestion, with instructor verification of accuracy |
| Quiz questions and answers | Generation from lecture content, with instructor validation of correctness |
| Lab instructions and code examples | Scaffolding, then instructor testing and correction |
| Python exec simulations | Initial implementation, reviewed for correctness |
What AI was not used for
- Grading student work
- Evaluating team projects
- Making academic integrity decisions
- Any personalised assessment or feedback to individual students
AI can be wrong
Language models make mistakes — factual errors, outdated information, and subtle conceptual inaccuracies do occur. Course content has been reviewed, but if you find an error, please open an issue or pull request on the course repository. Corrections are welcome and credited.
Student AI Policy
The use of AI tools (ChatGPT, Claude, GitHub Copilot, etc.) in coursework is permitted with the following conditions:
- You are responsible for everything you submit. AI output must be reviewed, understood, and validated by you before submission. Submitting AI output you do not understand is academic dishonesty.
- Cite AI use. If a significant portion of your submission was AI-assisted, state this in your documentation — the same way you would cite any source.
- AI does not replace understanding. Exams and in-class activities are designed to assess your own knowledge. Relying on AI to do your hands-on labs without understanding what you built will fail you in those evaluations.
- Plagiarism rules still apply. Submitting AI-generated content verbatim from a shared prompt, or copying a peer's AI-assisted work, is plagiarism.
AI as a learning tool
The best use of AI in this course is as a tutor and rubber duck, not as a writer. Ask it to explain a concept a different way, to review your code for issues, or to generate a quiz question so you can test yourself. Avoid asking it to generate your deliverables for you — the practice of building is the point.
Content Accuracy & Currency
This course covers technologies — containerisation, microservices, cloud platforms, Kubernetes, Kafka, OpenTelemetry — that evolve rapidly. Best efforts are made to keep material current, but:
- Version numbers in code examples may lag behind latest releases. Always check official documentation for the current stable version.
- Cloud pricing estimates in the deployment class are illustrative and will differ from your actual AWS bill. Use the AWS Pricing Calculator for real estimates.
- API behaviour of third-party tools (Spring Boot, Confluent Kafka, Jaeger) may change across minor versions. If an example does not behave as documented, check the tool's changelog.
This documentation is maintained as an open-source project. The source is available at github.com/insper/platform.
License
Course material is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license unless otherwise noted.
You are free to share and adapt the material for any purpose, provided you give appropriate credit to the original authors and indicate if changes were made.
Code examples embedded in the course documentation are released under the MIT License.