Google Cloud for AI & Application Developers: Beginners

In this section, students will gain hands-on knowledge of how to deploy modern, containerized applications using Google Cloud Run in a fully managed serverless environment. The module walks through the complete deployment lifecycle from preparing and containerizing an application to configuring services, managing revisions, and enabling secure access. Students will learn how Cloud Run automatically scales applications based on incoming traffic, how to handle environment variables and service configurations, and how to monitor performance and logs effectively.

By the end of this section, students will be able to confidently deploy production-ready applications to Cloud Run, manage updates without downtime, control traffic between revisions, secure endpoints, and optimize scalability and cost-efficiency within a real-world cloud deployment workflow.

In this section, students will explore how Google Cloud Pub/Sub enables event-driven architectures by facilitating asynchronous communication between services. You will learn how to create topics and subscriptions, publish and consume messages, and integrate Pub/Sub with cloud-based applications to build scalable, loosely coupled systems. By the end of this lecture, students will be able to implement Pub/Sub to trigger workflows, process real-time events, and design reliable event-driven systems in a cloud environment.

In this section, students will be introduced to Google Vertex AI, understanding its core components, capabilities, and role within the Google Cloud ecosystem. They will explore how Vertex AI supports the end-to-end machine learning lifecycle, including data preparation, model training, evaluation, and deployment. By the end of this lecture, students will be able to navigate the Vertex AI interface, identify its key services, and confidently set up and manage basic AI workflows within a cloud environment.

In this capstone module, Putting It All Together: Building an AI-Powered Microservice, students will move from isolated concepts to full implementation by designing, developing, and deploying a production-ready AI-driven microservice. This section brings together cloud deployment, containerization, API integration, and AI model interaction into one cohesive, real-world solution.

Throughout the lectures, students will architect a scalable microservice, integrate an AI model or external AI API, structure clean request–response workflows, and containerize the application for deployment. They will learn how to manage dependencies, configure environment variables securely, connect cloud services, and ensure the microservice performs efficiently under real-world usage conditions. The module also emphasizes best practices such as modular code design, error handling, logging, and performance monitoring to ensure reliability and maintainability.

By the end of this section, students will be able to confidently build, package, deploy, and manage a fully functional AI-powered microservice in a cloud environment, understanding not just how the components work individually, but how they integrate into a scalable, production-level intelligent system.