Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence (AI). A main component of that toolbox is Google's Vertex AI platform, which can aid organizations in streamlining machine learning (ML) workflows and deliver better machine learning models. In this course, you'll be introduced to Vertex AI, beginning with an overview of its features, model deployment, and end-to-end workflow. Then you'll explore pros and cons of Vertex AI, how it can be used to accelerate development, and model selection, training, and evaluation considerations. Finally, you'll learn about Vertex AI integration, data preparation, feature engineering, model evaluation, and Vertex AI success stories.
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Syllabus
- discover the key concepts covered in this course
- outline the fundamentals of Vertex AI and its role in the machine learning (ML) life cycle
- identify key features of Vertex AI, including MLOps, AutoML, custom training, and interfaces
- deploy a basic model using Vertex AI
- automate the end-to-end ML workflow using Vertex AI
- identify advantages and benefits of using Vertex AI for developing and deploying ML models
- outline how Vertex AI accelerates model development and deployment processes
- select, train, tune, and evaluate models using Vertex AI
- integrate Vertex AI with popular artificial intelligence frameworks
- identify best practices for data preparation, feature engineering, and model evaluation with Vertex AI
- outline real-world use cases and success stories of organizations using Vertex AI
- summarize the key concepts covered in this course