Aditya Singh
成为会员时间:2022
青铜联赛
800 积分
成为会员时间:2022
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.
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
完成 在 Compute Engine 實作負載平衡功能 技能徽章入門課程,即可證明您具備下列技能: 編寫 gcloud 指令和使用 Cloud Shell、在 Compute Engine 建立及部署虛擬機器, 以及設定網路和 HTTP 負載平衡器。 「技能徽章」是 Google Cloud 核發的 獨家數位徽章,用於肯定您在 Google Cloud 產品與服務方面的精通程度, 代表您已通過測驗,能在互動式實作環境中應用相關 知識。完成這個課程及挑戰研究室 最終評量,即可取得技能徽章並與親友分享。
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.