Vertex AI Foundations: Build and Deploy Machine Learning Models

Machine learning projects often struggle due to the complexity of managing infrastructure, training pipelines, and deployments. Vertex AI simplifies this journey by providing a unified platform to build, deploy, and scale models efficiently. This text-based course guides you through the core components of the platform, helping you transition from conceptual machine learning to production-ready workflows. By completing this course, you will understand how to manage datasets, train custom models, and utilize modern generative AI features within a unified ecosystem. You will gain the practical knowledge needed to streamline your machine learning workflows from start to finish. What you'll learn: - Understand the fundamental architecture and core services of the Vertex AI platform. - Prepare and manage datasets for training tabular, image, and text models. - Train machine learning models using both automated tools and custom training pipelines. - Deploy trained models to scalable endpoints for real-time and batch predictions. - Explore generative AI capabilities, including foundational models and prompt engineering basics. - Apply modern MLOps best practices to monitor and manage your model's lifecycle. The course begins with foundational concepts and platform navigation before guiding you through data preparation, model training, and deployment. You will then explore advanced topics like pipeline automation and generative AI integration. This course is designed for aspiring data scientists, software developers, and cloud enthusiasts who are new to machine learning operations. No prior experience with the platform is required, though a basic understanding of cloud concepts is helpful. Start reading today to master cloud-based machine learning workflows.