加入 登录

Sam Hancock

成为会员时间：2025

钻石联赛

6779 积分
Natural Language Processing on Google Cloud Earned Sep 15, 2025 EDT
面向机器学习的 BigQuery Earned Sep 11, 2025 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Sep 10, 2025 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jun 20, 2025 EDT
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估 Earned Jun 16, 2025 EDT
MLOps with Agent Platform: Manage Features Earned Jun 16, 2025 EDT
Machine Learning Operations (MLOps): Getting Started Earned Jun 12, 2025 EDT
Build a Certification Study Guide: PMLE Earned May 14, 2025 EDT

This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

了解详情

想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。

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

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

本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。 学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。

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 Agent Platform 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

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.

了解详情