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Shahenshah Ali Syed

成为会员时间：2021

青铜联赛

7060 积分
Google Cloud: Prompt Engineering Guide Earned Aug 10, 2025 EDT
在 Vertex AI 使用 Gemini API 探索生成式 AI Earned Nov 9, 2024 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning Earned Nov 9, 2024 EST
Preparing for your Professional Data Engineer Journey Earned Oct 14, 2024 EDT
生成式 AI 簡介 Earned Feb 23, 2024 EST
Building Batch Data Pipelines on Google Cloud Earned Aug 1, 2022 EDT
Serverless Data Processing with Dataflow: Foundations Earned Apr 5, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Apr 1, 2022 EDT
Building Resilient Streaming Analytics Systems on Google Cloud Earned Mar 31, 2022 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Mar 28, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Mar 24, 2022 EDT

Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.

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完成「在 Vertex AI 使用 Gemini API 探索生成式 AI」技能徽章中階課程，即可證明自己具備下列技能： 可運用 Gemini API 生成文字、分析圖片和影片來強化內容創作能力，還能使用函式呼叫技巧。 本課程將帶您瞭解如何善用進階的 Gemini 技術、使用多模態內容生成功能，並提升 AI 專案的潛力。 通過實作實驗室和挑戰評量，就能獲得技能徽章，證明自己擁有特定產品的 實作知識。完成課程或直接進行挑戰實驗室， 即可取得徽章。徽章可證明您的專業能力、 提升專業形象，開創更多職涯發展機會。 已獲得的徽章會顯示在您的個人資料 中。

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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。

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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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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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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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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.

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