Which stage of the machine learning workflow includes model training and evaluation?
本练习是课程的一部分
通过我们的互动练习之一，将理论转化为实践
This lesson guides learners through the course structure, which is built upon a three-layer AI framework: AI infrastructure, development, and solutions. It outlines the learning objectives and introduces learners to Google's comprehensive suite of full-stack AI development tools.
This module begins with a use case demonstrating the AI capabilities. It then focuses on the AI infrastructure like compute and storage. It also explains the primary data and AI development products on Google Cloud. Finally, it demonstrates how to use BigQuery ML to build an ML model, which helps transition from data to AI.
This module introduces generative AI (gen AI), the latest AI advancement, and the Google Cloud toolkits for developing gen AI projects. It starts by examining the foundation models. It then investigates the prompt-to-production lifecycle with Vertex AI Studio, including prompt engineering, app deployment, and model tuning. Additionally, this module explores AI agents and Google’s full stack of AI agent development tools.
This module explores the various options for developing an AI project on Google Cloud, from ready-made solutions like pre-trained APIs, to no-code and low-code solutions like AutoML, and code-based solutions like custom training. It compares the advantages and disadvantages of each option to help decide the right development tools.
This module walks through the ML workflow from data preparation, to model development, and to model serving on Vertex AI. It also illustrates how to convert the workflow into an automated pipeline using Vertex AI Pipelines.
当前练习
This lesson summarizes the course by addressing the most important concepts, tools, technologies, and products for each module.
