In this module, we will explore key cloud-native concepts critical to building scalable and resilient applications. You will gain a clear understanding of cloud scalability, architecture paradigms, and when to apply serverless versus container-based solutions. These foundational principles are essential for deploying data-driven systems efficiently in the cloud.

In this module, we will guide you through setting up your GCP environment, including account creation and CLI installation. You'll also gain hands-on experience with gcloud and gsutil, empowering you to navigate and manage cloud resources using terminal-based commands. This is your launchpad for all GCP-related tasks ahead.

In this module, we will take a deep dive into containerization with Docker and deploying applications using Cloud Run. You'll work through hands-on labs that show how to run containers, integrate with the Container Registry, and automate deployments with Cloud Build. This prepares you for efficient serverless deployments in real-world scenarios.

In this module, we will explore Google App Engine and its capabilities for deploying scalable serverless applications. You’ll gain experience deploying Python applications, integrating with BigQuery, and improving efficiency through caching. These skills enable you to build optimized applications tailored for serverless environments.

In this module, we will focus on building event-driven applications using Google Cloud Functions. You'll learn to deploy and trigger functions via different GCP services and apply this knowledge in a complete use-case deployment. This helps you create responsive, scalable architectures driven by events.

In this module, we will guide you through deploying machine learning models using App Engine. From understanding model lifecycles to solving a fraud detection use case, you’ll learn how to train, validate, and serve models in a production-grade serverless environment.

In this module, we will work with Dataproc to run serverless PySpark jobs for large-scale data processing. You'll learn about cluster persistence, monitoring tools, and how to automate workflows using Airflow. This equips you with the tools to manage big data pipelines in a cloud-native way.

In this module, we will explore Vertex AI, Google’s fully managed ML platform. You’ll gain hands-on experience with model training, deployment, and pipeline orchestration using both the UI and SDK. This section is essential for productionizing ML workflows on Google Cloud.

In this module, we will explore how to schedule jobs and monitor applications using Google Cloud tools. You’ll learn how to set up alerts for App Engine, Cloud Run, and Cloud Functions to maintain reliability and observability in production systems. This ensures operational excellence for your deployed applications.