Getting Started with Notebooks in Vertex AI

Developing machine learning models requires a reliable, scalable environment where you can write code, analyze data, and collaborate. Vertex AI Notebooks provides a powerful Jupyter-based platform in the cloud to streamline your entire data science workflow. In this text-only course, you will learn how to set up, manage, and utilize cloud-based Jupyter notebooks to run data science experiments. You will transition from local coding to cloud-powered development, mastering the essential tools to prepare data, train models, and understand modern cloud workflows. What you'll learn: - Understand Vertex AI notebook instances and core cloud infrastructure concepts - Configure custom Jupyter environments with specific libraries and virtual environments - Import, clean, and analyze datasets using modern Python data libraries - Implement version control and collaboration best practices for notebooks - Connect notebooks to cloud storage and data warehousing tools securely - Transition code from experimental notebooks to reproducible machine learning pipelines The course begins with foundational concepts of cloud notebooks, security, and interface navigation. You will then progress through step-by-step written explanations covering data access, environment customization, and integrated machine learning workflows. This course is designed for aspiring data scientists, machine learning enthusiasts, and developers who are new to cloud-based development environments. No prior experience with Vertex AI is required, though a basic understanding of Python is helpful. Start reading today to build and manage your own cloud-hosted Jupyter notebooks for data science.