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Menjadi anggota sejak 2023

Silver League

20696 poin
Agen AI Perusahaan yang Aman Earned Okt 14, 2026 EDT
Membangun Sistem Multi-Agen (MAS) Kolaboratif dengan ADK & MCP Earned Okt 6, 2026 EDT
Mengelola Kubernetes di Google Cloud Earned Feb 5, 2025 EST
Mengimplementasikan Pipeline CI/CD di Google Cloud Earned Jan 28, 2025 EST
Menyiapkan Data untuk ML API di Google Cloud Earned Jun 4, 2024 EDT
[DEPRECATED] Gemini untuk Data Scientist dan Analis Earned Mei 24, 2024 EDT
Serverless Data Processing with Dataflow: Operations Earned Okt 27, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Okt 3, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Okt 2, 2023 EDT
Introduction to Vertex Forecasting and Time Series in Practice Earned Sep 29, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Sep 29, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 15, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Agu 11, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Agu 4, 2023 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Jul 20, 2023 EDT

Pelajari cara mengamankan dan mengatur agen AI di Google Cloud. Kursus ini memberikan roadmap praktis untuk mengamankan memori agen, menerapkan kontrol akses yang ketat dengan IAM dan Kontrol Layanan VPC, serta men-deploy batasan percakapan real-time untuk memastikan deployment perusahaan yang aman dan patuh.

Pelajari lebih lanjut

Pelajari cara mendesain, mengoordinasikan, dan men-deploy sistem multi-agen (MAS) menggunakan Agent Development Kit (ADK) Google dan Model Context Protocol (MCP). Dengan menggunakan contoh concierge perjalanan, Anda akan mempelajari cara mengorkestrasi jaringan agen khusus dengan batasan kustom. Di akhir kursus, Anda akan memahami pola desain dan strategi deployment yang diperlukan untuk alur kerja AI yang tangguh dan skalabel.

Pelajari lebih lanjut

Selesaikan kursus badge keahlian tingkat menengah Mengelola Kubernetes di Google Cloud untuk menunjukkan keterampilan dalam hal berikut: mengelola deployment dengan kubectl, memantau serta menjalankan proses debug aplikasi di Google Kubernetes Engine (GKE), dan menggunakan teknik continuous delivery.

Pelajari lebih lanjut

Dapatkan badge keahlian tingkat menengah dengan menyelesaikan kursus badge keahlian Mengimplementasikan Pipeline CI/CD di Google Cloud. tempat Anda mempelajari cara menggunakan Artifact Registry, Cloud Build, dan Cloud Deploy. Anda berinteraksi dengan konsol Google Cloud, Google Cloud CLI, Cloud Run, dan GKE. Kursus ini mengajarkan cara membangun pipeline continuous integration pipeline, menyimpan dan mengamankan artefak, memindai kerentanan, membuktikan validitas rilis yang disetujui. Selain itu, Anda akan mendapatkan pengalaman langsung dalam men-deploy aplikasi ke GKE dan Cloud Run.

Pelajari lebih lanjut

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 Managed Service for Apache Spark, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API.

Pelajari lebih lanjut

Dalam kursus ini, Anda akan mempelajari bagaimana Gemini, kolaborator yang didukung AI generatif dari Google Cloud, membantu menganalisis data pelanggan dan memprediksi penjualan produk. Anda juga akan mempelajari cara mengidentifikasi, mengategorikan, dan mengembangkan pelanggan baru menggunakan data pelanggan di BigQuery. Dengan menggunakan lab interaktif, Anda akan melihat bagaimana Gemini meningkatkan analisis data dan alur kerja machine learning. Duet AI berganti nama menjadi Gemini, yang merupakan model generasi berikutnya dari kami.

Pelajari lebih lanjut

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

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.

Pelajari lebih lanjut

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

Pelajari lebih lanjut