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ibrahim elsanhouri

Member since 2025

Diamond League

3941 points
NCAA® March Madness®: Bracketology with Google Cloud Earned Mar 29, 2026 EDT
Introduction to Vertex Forecasting and Time Series in Practice Earned Mar 27, 2026 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Mar 12, 2026 EDT
透過 BigQuery 機器學習執行推論作業 Earned Mar 11, 2026 EDT
以串流方式將分析資料傳入 BigQuery Earned Mar 11, 2026 EDT
Machine Learning Operations (MLOps): Getting Started Earned Mar 11, 2026 EDT
Working with Notebooks in Vertex AI Earned Mar 10, 2026 EDT
使用 BigQuery 進行機器學習 Earned Mar 9, 2026 EDT
Build Streaming Data Pipelines on Google Cloud Earned Feb 9, 2026 EST

In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

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This course is an introduction to building forecasting solutions with Google Cloud. You start with sequence models and time series foundations. You then walk through an end-to-end workflow: from data preparation to model development and deployment with Vertex AI. Finally, you learn the lessons and tips from a retail use case and apply the knowledge by building your own forecasting models.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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瞭解如何將 BigQuery 機器學習用於推論、資料分析師應使用這項工具的原因、相關應用實例，以及支援的機器學習模型。您也將瞭解如何在 BigQuery 建立和管理這些機器學習模型。

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完成「以串流方式將分析資料傳入 BigQuery」課程，即可獲得技能徽章。 在此課程中，您將綜合應用 Pub/Sub、Dataflow 和 BigQuery，並以串流方式傳送 資料進行分析。

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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.

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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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