Do I need prior experience building SaaS applications?

No prior SaaS experience is required. The course builds the application from scratch, starting with MVP planning and progressing through API development, containerization, and marketing site deployment. Basic Python proficiency and familiarity with the command line are sufficient.

What technologies does this course use?

The course uses Python with FastAPI for API development, pytest for automated testing, Docker for containerization, GitHub Pages for static site hosting, and Hypertext Transfer Protocol (HTTP) API key authentication for access control. LLM tools assist with code generation and architectural planning throughout the course.

Will I have a deployable product by the end of the course?

Yes. The course produces a containerized API service published to a container registry, a marketing website with landing and pricing pages deployed on GitHub Pages, API key authentication for metered access, and developer documentation. The capstone project integrates all components into a production-ready SaaS application.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Build a Production SaaS Application with AI

本课程是 AI Tooling 专项课程的一部分

位教师：Alfredo Deza

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Apply MVP planning and API design patterns to build a documented, tested application from initial project structure through automated verification
Evaluate containerization strategies, automating container builds with CI pipelines, and publishing production images to a container registry
Analyze and design conversion-focused landing pages, implement API key authentication for monetization, and deploy sites with developer docs

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Tooling 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Learn to build and launch a complete Software as a Service (SaaS) application using AI-assisted development techniques. This course walks through the entire product lifecycle, from planning a Minimum Viable Product (MVP) to deploying a monetized Application Programming Interface (API) service. You will build a Python API using FastAPI, define data models, create documented endpoints, and verify behavior with an automated pytest test harness. The course covers Docker containerization from Dockerfile creation through container testing, automated builds via Continuous Integration (CI) pipelines, and publishing images to a container registry for production distribution. In the second module, you will build the go-to-market foundation: designing conversion-focused landing pages, structuring pricing tiers, deploying a marketing site to GitHub Pages, implementing API key authentication for metered access, and writing developer documentation that drives adoption. Throughout the course, Large Language Model (LLM) tools accelerate development from architecture planning through code generation. By completing this course, you will have the skills to take an AI-powered SaaS product from concept to production launch.