(Previously named Search on Vertex AI Agent Builder) Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities. This course teaches you to use Vertex AI Agent Builder to integrate enterprise-grade generative AI search.
Kursus ini memperkenalkan Vertex AI Vector Search dan menjelaskan bagaimana layanan ini dapat digunakan untuk membangun aplikasi penelusuran dengan API model bahasa besar (LLM) untuk embedding. Kursus ini terdiri atas materi konseptual terkait penelusuran vektor dan embedding teks, demo praktis tentang cara membangun penelusuran vektor di Vertex AI, serta lab interaktif.
Kursus ini memperkenalkan model difusi, yaitu kelompok model machine learning yang belakangan ini menunjukkan potensinya dalam ranah pembuatan gambar. Model difusi mengambil inspirasi dari fisika, khususnya termodinamika. Dalam beberapa tahun terakhir, model difusi menjadi populer baik di dunia industri maupun penelitian. Model difusi mendasari banyak alat dan model pembuatan gambar yang canggih di Google Cloud. Kursus ini memperkenalkan Anda pada teori yang melandasi model difusi dan cara melatih serta men-deploy-nya di Vertex AI.
Kursus ini memperkenalkan Vertex AI Studio, sebuah alat untuk membuat prototipe dan menyesuaikan model AI generatif. Melalui materi yang imersif, demo yang menarik, dan lab interaktif, Anda akan mengeksplorasi alur kerja AI generatif dan mempelajari cara memanfaatkan Vertex AI Studio untuk aplikasi multimodal Gemini, desain perintah, dan penyesuaian model. Tujuannya adalah agar Anda dapat memanfaatkan potensi model tersebut dalam project dengan Vertex AI Studio.
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.
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.
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.
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.
Complete the intermediate Mitigate Threats and Vulnerabilities with Security Command Center skill badge to demonstrate skills in the following: preventing and managing environment threats, identifying and mitigating application vulnerabilities, and responding to security anomalies.
This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.
Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.
Earn a DRI badge by completing the Application Modernization - GKE multi-cluster multi-region infrastructure quest, where you demonstrate your understanding of GKE Best-practices GKE Networking, GKE Security, GKE multi-clusters architecture, VPC networking custom networking mode and VPC routing When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page.
Earn a DRI badge by completing the Infra Foundations - Implementing Least Privilege for Service Accounts quest, where you demonstrate your capabilities to manage service accounts, assign IAM roles, setting up and using impersonation and implementing logging sinks that target GCS buckets. When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page.
Earn a DRI badge by completing the Infra Foundations - Implementing Private Google Access for VPC Service Controls quest, where you demonstrate your capabilities implementing VPC networking custom networking mode, Private Google Access, Cloud DNS response policies, VPC routing and Service Controls. When you complete this activity, you can earn the badge displayed above! View all the badges you have earned by visiting your profile page.
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.
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.
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.
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.
This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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.
Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Flex your Google Clout! Each week unlocks a new cloud puzzle. How fast can you find the solution? Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Flex your Google Clout! Each day unlocks a new cloud puzzle. Complete all five and you’ll earn the inaugural Google Cloud badge! Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
Flex your Google Clout! Each day unlocks a new cloud puzzle. Complete all five and you’ll earn the inaugural Google Cloud badge! Share your score on your choice of social networks and join the conversation over in the Google Cloud Community.
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.
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.
Google Cloud, in partnership with Harvard Global Health Institute, released the COVID-19 Public Forecasts to serve as an additional resource for healthcare, the public sector, and other organizations responding to the pandemic. In this edition of the Work and Play Series, we are going to learn how to use that data to make predictions and visualizations. You’ll get hands-on experience with exactly the same tools being used to help solve complex problems and keep people safe and healthy.
Earn a skill badge by completing the Build and Secure Networks in Google Cloud course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud. 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 skill badge, and final assessment challenge lab, to receive a digital badge that you can share with your network.