Deployment and model tuning

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Este exercicio faz parte do curso

Introduction to AI and Machine Learning on Google Cloud

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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.

Exercise 1: Course introduction

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.

Exercise 1: A use case Exercise 2: AI on Google Cloud Exercise 3: AI infrastructure Exercise 4: AI models Exercise 5: BigQuery ML Exercise 6: Predict Visitor Purchases with BigQuery ML Exercise 7: Summary Exercise 8: Quiz Question 1 Exercise 9: Quiz Question 2 Exercise 10: Quiz Question 3 Exercise 11: Quiz Question 4 Exercise 12: Quiz Question 5 Exercise 13: Reading

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.

Exercise 1: Generative AI on Google Cloud Exercise 2: Foundation models Exercise 3: Idea to app Exercise 4: Prompt engineering Exercise 5: Deployment and model tuning

Exercicio Atual

Exercise 6: Get Started with Agent Studio Exercise 7: AI agents Exercise 8: Agent building with Google Cloud Exercise 9: Summary Exercise 10: Quiz Question 1 Exercise 11: Quiz Question 2 Exercise 12: Quiz Question 3 Exercise 13: Quiz Question 4 Exercise 14: Quiz Question 5 Exercise 15: Reading List

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.

Exercise 1: AI developement options Exercise 2: Vertex AI Exercise 3: AutoML Exercise 4: Pre-trained APIs Exercise 5: Custom training Exercise 6: Lab introduction Exercise 7: Entity and Sentiment Analysis with the Natural Language API Exercise 8: Summary Exercise 9: Quiz Question 1 Exercise 10: Quiz Question 2 Exercise 11: Quiz Question 3 Exercise 12: Quiz Question 4 Exercise 13: Quiz Question 5 Exercise 14: Quiz Question 6 Exercise 15: Reading

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.

Exercise 1: ML workflow Exercise 2: Data preparation Exercise 3: Model development Exercise 4: Model serving Exercise 5: MLOps and workflow automation Exercise 6: Lab introduction (optional)Exercise 7: Agent Platform: Predicting Loan Risk with AutoML Exercise 8: How a machine learns (optional)Exercise 9: Summary Exercise 10: Quiz Question 1 Exercise 11: Quiz Question 2 Exercise 12: Quiz Question 3 Exercise 13: Quiz Question 4 Exercise 14: Quiz Question 5 Exercise 15: Reading

This lesson summarizes the course by addressing the most important concepts, tools, technologies, and products for each module.

Exercise 1: Course summary Exercise 2: Reading Exercise 3: Course resources