加入 登录

Michelangelo Martorana

成为会员时间：2023

适用于生成式 AI 的机器学习运维 (MLOps) Earned Aug 20, 2025 EDT
大型语言模型简介 Earned Aug 20, 2025 EDT
生成式 AI 简介 Earned Aug 20, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Aug 19, 2025 EDT
Machine Learning Operations (MLOps): Getting Started Earned Aug 19, 2025 EDT
Feature Engineering Earned Jul 7, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned May 26, 2023 EDT
Data Lake Modernization on Google Cloud: Cloud Composer Earned Apr 11, 2023 EDT
How Google Does Machine Learning Earned Mar 14, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Mar 6, 2023 EST

本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。

了解详情

这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

了解详情

这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

了解详情

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

了解详情

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

了解详情

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

了解详情

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

了解详情

Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.

了解详情

This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

了解详情

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

了解详情