Google Cloud Big Data and Machine Learning Fundamentals
Earned Agu 8, 2022 EDT
Complete the Build basic Conversational Agents with Playbooks and Flows skill badge to demonstrate your proficiency in building virtual agents using traditional NLU and generative-based features.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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In this course, you'll learn to develop generative agents that answer questions using websites, documents, or structured data. You will explore Vertex AI Applications and understand the advantages of data store agents, including their scalability and security.
You'll learn about different data store types and also discover how to connect data stores to agents and add personalization for enhanced responses.
Finally, you'll gain insights into common search configurations and troubleshooting techniques.
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Explore the Generative AI features for Conversational Agents and how to incorporate them into stateful Flows.
Discover the possibilities with Generators, Generative Fallback, and Data Stores, as well as best practices and security settings for using these features.
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Discover flows in Conversational Agents and learn how to build deterministic chat and voice experiences with language models. Explore key concepts like drivers, intents, and entities, and how to use them to create conversational agents.
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Explore Playbooks and their implementation of the ReAct pattern for building Conversational Agents. You will learn how to construct a Playbook, set up goals and instructions to build a chatbot in natural language, and learn to test and deploy your solution.
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This is an introductory course to all solutions in the Conversational AI portfolio and the Gen AI features that are available to transform them. The course also explores the business case around Conversational AI, and the use cases and user personas addressed by the solution. Please note Dialogflow CX was recently renamed to Conversational Agents and this course is in the process of being updated to reflect the new product name for Dialogflow CX.
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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.
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Menjelajahi teknologi, alat, dan aplikasi penelusuran yang didukung AI dalam kursus ini. Mempelajari penelusuran semantik dengan memanfaatkan embedding vektor, penelusuran campuran yang menggabungkan pendekatan semantik dan kata kunci, serta Retrieval-Augmented Generation (RAG) yang meminimalkan halusinasi AI sebagai agen AI yang di-grounding. Mendapatkan pengalaman praktis dengan Vertex AI Vector Search untuk membangun mesin telusur yang cerdas.
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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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Kursus ini memperkenalkan Vertex AI Studio, sebuah alat untuk berinteraksi dengan model AI generatif, membuat prototipe ide bisnis, dan meluncurkannya ke dalam produksi. Melalui kasus penggunaan yang imersif, pelajaran menarik, dan lab interaktif, Anda akan menjelajahi siklus proses dari perintah ke produk dan mempelajari cara memanfaatkan Vertex AI Studio untuk aplikasi multimodal Gemini, desain perintah, rekayasa perintah, dan tuning model. Tujuan kursus ini adalah agar Anda dapat memanfaatkan potensi AI generatif dalam project Anda dengan Vertex AI Studio.
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Selesaikan badge keahlian pengantar Desain Perintah dalam Vertex AI untuk menunjukkan keterampilan Anda dalam hal berikut: rekayasa perintah, analisis gambar, dan teknik generatif multimodal, dalam Vertex AI. Pelajari cara membuat perintah yang efektif, memandu output AI generatif, dan menerapkan model Gemini dalam skenario pemasaran di dunia nyata. Badge keahlian merupakan badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir untuk menerima badge keahlian yang dapat Anda bagikan kepada jaringan Anda.
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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Seiring semakin meningkatnya penggunaan Kecerdasan Buatan dan Machine Learning di kalangan perusahaan, proses membangunnya secara bertanggung jawab juga menjadi semakin penting. Membicarakan responsible AI mungkin lebih mudah bagi banyak orang daripada mempraktikkannya. Jika Anda tertarik untuk mempelajari cara mengoperasionalkan responsible AI dalam organisasi Anda, kursus ini cocok untuk Anda.
Dalam kursus ini, Anda akan mempelajari bagaimana Google Cloud mengoperasionalkan responsible AI, dengan praktik terbaik dan pelajaran yang dapat dipetik. Hal ini berguna sebagai framework bagi Anda untuk membangun pendekatan responsible AI.
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Ini adalah kursus pengantar pembelajaran mikro yang dimaksudkan untuk menjelaskan responsible AI, alasan pentingnya responsible AI, dan cara Google mengimplementasikan responsible AI dalam produknya. Kursus ini juga memperkenalkan 7 prinsip AI Google.
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Ini adalah kursus pengantar pembelajaran mikro yang membahas definisi model bahasa besar (LLM), kasus penggunaannya, dan cara menggunakan prompt tuning untuk meningkatkan performa LLM. Kursus ini juga membahas beberapa alat Google yang dapat membantu Anda mengembangkan aplikasi AI Generatif Anda sendiri.
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Ini adalah kursus pengantar pembelajaran mikro yang bertujuan untuk mendefinisikan AI Generatif, cara penggunaannya, dan perbedaannya dari metode machine learning konvensional. Kursus ini juga mencakup Alat-alat Google yang dapat membantu Anda mengembangkan aplikasi AI Generatif Anda sendiri.
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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Selesaikan badge keahlian tingkat menengah Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML untuk menunjukkan keterampilan Anda dalam hal berikut: membangun pipeline transformasi data ke BigQuery dengan Dataprep by Trifacta; menggunakan Cloud Storage, Dataflow, dan BigQuery untuk membangun alur kerja ekstrak, transformasi, dan pemuatan (ETL); serta membangun model machine learning menggunakan BigQuery ML.
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Selesaikan badge keahlian pengantar Menyiapkan Data untuk ML API di Google Cloud untuk menunjukkan keterampilan Anda dalam hal berikut: menghapus data dengan Dataprep by Trifacta, menjalankan pipeline data di Dataflow, membuat cluster dan menjalankan tugas Apache Spark di Dataproc, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud s ebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir, untuk menerima badge keahlian yang dapat Anda bagikan dengan jaringan Anda.
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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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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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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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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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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.
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Selesaikan pengantar badge keahlian Mengimplementasikan Load Balancing di Compute Engine untuk menunjukkan keterampilan berikut ini: menulis perintah gcloud dan menggunakan Cloud Shell, membuat dan men-deploy virtual machine di Compute Engine, serta mengonfigurasi jaringan dan load balancer HTTP. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan yang interaktif. Selesaikan badge keahlian ini, dan penilaian akhir Challenge Lab, untuk menerima badge keahlian yang dapat Anda bagikan dengan jaringan Anda.
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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.
Build basic Conversational Agents with Playbooks and Flows
Earned Sep 11, 2025 EDT
Virtual FAQ with data store agents
Earned Sep 11, 2025 EDT
Incorporate Generative Features into Conversational Agent flows
Earned Sep 11, 2025 EDT
Stateful Flows
Earned Sep 10, 2025 EDT
Generative Playbooks
Earned Sep 9, 2025 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned Agu 27, 2025 EDT
Build and Deploy a Generative AI solution using a RAG framework
Earned Sep 17, 2024 EDT
Penelusuran Vektor dan Embedding
Earned Agu 26, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Agu 22, 2024 EDT
Text Prompt Engineering Techniques
Earned Agu 14, 2024 EDT
Generative AI Fundamentals
Earned Agu 14, 2024 EDT
Pengantar Vertex AI Studio
Earned Agu 12, 2024 EDT
Desain Perintah dalam Vertex AI
Earned Agu 9, 2024 EDT
Generative AI for Business Leaders
Earned Jul 25, 2024 EDT
Responsible AI: Menerapkan Prinsip AI dengan Google Cloud
Earned Jul 2, 2024 EDT
Pengantar Responsible AI
Earned Mei 8, 2024 EDT
Pengantar Model Bahasa Besar
Earned Mei 7, 2024 EDT
Pengantar AI Generatif
Earned Mei 6, 2024 EDT
Cloud Foundations: Customer Onboarding Best Practices
Earned Mar 7, 2023 EST
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML
Earned Nov 2, 2022 EDT
Menyiapkan Data untuk ML API di Google Cloud
Earned Okt 31, 2022 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Okt 10, 2022 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Sep 19, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Agu 24, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Agu 20, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Agu 17, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Agu 14, 2022 EDT
Mengimplementasikan Load Balancing di Compute Engine
Earned Agu 9, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Agu 8, 2022 EDT