Summary
1. Summary
SPEAKER: Hope you enjoyed this module on amazing generative AI, which not only creates content but also takes action for you. You were first introduced to Google's three-layered Gen AI stack-- foundation models, Gen AI development tools, and Gen AI applications. You started with foundation models, the backbone of Gen AI. This section explained the foundation models and detailed Google's general purpose model, the Gemini family, and specialized models like Imagen and Veo. You also learned about Gemini's multimodal capabilities and how to access and tailor these models. You then explored Vertex AI Studio and the prompt-to-production lifecycle. You joined Bea, Ann, and Ian from Cymbal Insurance who quickly turned an idea into an application and built a prototype where AI helps with insurance risk analysis. In this process, you learned what components make a good prompt, task, context, and examples. After a quick idea-to-app journey, you dived into the first half of the life cycle, prompt engineering. This involved prompt design, evaluation, and refinement, enhanced by Vertex AI studio's features like prompt templates, model parameter specification, such as temperature, topK, topP, and side-by-side prompt comparison. You then proceeded to the second half of the life cycle, deployment and model tuning. Vertex AI Studio streamlines app development with automated code generation and seamless integration with Cloud Run and Cloud Shell. You also learned about grounding and RAG for improved model accuracy alongside various tuning techniques, including prompt design, parameter-efficient tuning, and full fine-tuning. Next, the journey got more exciting as you were introduced to AI agents as the next evolution of Gen AI, moving beyond chatbots and enabling AI to take action, automate workflows, and make decisions. This defines an AI agent by its three core components-- the model or brain; tools, like hands and feet to connect with the external world; and orchestration layer, or the nervous system. With the idea of AI agents and agentic AI, the last lesson provided a practical guide on building AI agents on Google Cloud. It highlighted the comprehensive suite of tools offered across Google's generative AI architecture, including Vertex AI Model Garden for accessing and fine-tuning foundation models, Vertex AI Agent Builder for developers to construct AI agents, and both Gemini Enterprise and NotebookLM for end users to develop no-code agents. This lesson also offered a decision tree to help navigate these tools. Already impressed by the leaps in generative AI? It's time to channel that excitement into solving real-world business challenges. In our next module, prepare to dive even deeper into building a machine learning model.2. Let's practice!
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