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Terapkan keterampilan Anda di Konsol Google Cloud

Francisco Colomer

Menjadi anggota sejak 2023

Diamond League

43048 poin
Google DeepMind: 02 Represent Your Language Data Earned Jan 9, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model Earned Jan 4, 2026 EST
Discover Business Value for Customers Earned Des 31, 2025 EST
Partner Pre-Sales Readiness Training Earned Des 26, 2025 EST
Modernizing Mainframe Applications with Google Cloud Earned Des 22, 2025 EST
Membuat dan Mengelola Instance AlloyDB Earned Des 19, 2025 EST
Membuat dan Mengelola Instance Bigtable Earned Des 17, 2025 EST
Membuat dan Mengelola Instance Cloud Spanner Earned Des 15, 2025 EST
Membuat dan Mengelola Instance Cloud SQL untuk PostgreSQL Earned Des 11, 2025 EST
Pengantar Vertex AI Studio Earned Des 5, 2025 EST
Pengantar AI Generatif Earned Des 4, 2025 EST
Introduction to Reliable Deep Learning Earned Nov 30, 2025 EST
Memigrasikan Data MySQL ke Cloud SQL Menggunakan Database Migration Service Earned Nov 27, 2025 EST
Enterprise Database Migration Earned Nov 25, 2025 EST
Select a Google Cloud Database for Your Applications Earned Sep 24, 2025 EDT
Dasar-Dasar Google Cloud: Infrastruktur Inti Earned Jul 26, 2025 EDT
Machine Learning in the Enterprise Earned Sep 29, 2024 EDT
How Google Does Machine Learning Earned Mei 29, 2024 EDT
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML Earned Nov 15, 2023 EST
Membangun Data Warehouse dengan BigQuery Earned Nov 14, 2023 EST
Menyiapkan Data untuk ML API di Google Cloud Earned Nov 13, 2023 EST
Serverless Data Processing with Dataflow: Operations Earned Nov 9, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Nov 4, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Okt 26, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Okt 23, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Okt 22, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Okt 20, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Okt 14, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Okt 12, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Okt 4, 2023 EDT

In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.

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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.

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The course aims to train Google technical sales partners on the business value discovery process using proprietary content. Course activities use an external tool (Yoodli). Refer to Yoodli's Terms of Service and Privacy Notice.

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Want to learn more about Google Cloud? Grow your Google Cloud knowledge, strengthen your skills to win with customers, and scale your Google Cloud business. Find it here in one handy location.

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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.

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Selesaikan badge keahlian pengantar Membuat dan Mengelola Instance AlloyDB untuk menunjukkan keterampilan dalam hal berikut: melakukan operasi inti AlloyDB dan tugas, bermigrasi ke AlloyDB dari PostgreSQL, mengelola database AlloyDB, dan mempercepat kueri analisis menggunakan Columnar Engine AlloyDB.

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Selesaikan badge keahlian pengantar Membuat dan Mengelola Instance Bigtable untuk menunjukkan keterampilan dalam hal berikut: membuat instance, mendesain skema, mengkueri data, dan melakukan tugas administratif di Bigtable termasuk memantau performa serta mengonfigurasi penskalaan otomatis dan replikasi node.

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Dapatkan badge keahlian dengan menyelesaikan kursus tentang pengantar Membuat dan Mengelola Instance Cloud Spanner untuk menunjukkan keterampilan dalam: membuat dan berinteraksi dengan instance dan database Cloud Spanner; memuat database Cloud Spanner menggunakan berbagai teknik; mencadangkan database Cloud Spanner; menentukan skema dan memahami rencana kueri; serta men-deploy Aplikasi Web Modern yang terhubung ke instance Cloud Spanner.

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Selesaikan badge keahlian pengantar Membuat dan Mengelola Instance Cloud SQL untuk PostgreSQL untuk menunjukkan keterampilan dalam hal berikut: memigrasikan, mengonfigurasi, dan mengelola Instance dan database Cloud SQL untuk PostgreSQL.

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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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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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This course introduces you to the world of reliable deep learning, a critical discipline focused on developing machine learning models that not only make accurate predictions but also understand and communicate their own uncertainty. You'll learn how to create AI systems that are trustworthy, robust, and adaptable, particularly in high-stakes scenarios where errors can have significant consequences.

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Selesaikan kursus badge keahlian pengantar Memigrasikan Data MySQL ke Cloud SQL Menggunakan Database Migration Service untuk menunjukkan keterampilan dalam hal berikut: memigrasikan data MySQL ke Cloud SQL menggunakan berbagai jenis tugas dan opsi konektivitas yang tersedia dalam Database Migration Service dan memigrasikan data pengguna MySQL saat menjalankan tugas Database Migration Service.

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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.

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In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud. You explore relational and NoSQL databases, dive into Cloud SQL, AlloyDB, and Spanner, and learn how to align database strengths with your application requirements, including those of generative AI. Gain hands-on experience configuring Vector Search and migrating applications to the cloud.

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Dasar-Dasar Google Cloud: Infrastruktur Inti memperkenalkan konsep dan terminologi penting untuk bekerja dengan Google Cloud. Melalui video dan lab interaktif, kursus ini menyajikan dan membandingkan banyak layanan komputasi dan penyimpanan Google Cloud, bersama dengan resource penting dan alat pengelolaan kebijakan.

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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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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 tingkat menengah Membangun Data Warehouse dengan BigQuery untuk menunjukkan keterampilan Anda dalam hal berikut: menggabungkan data untuk membuat tabel baru, memecahkan masalah penggabungan, menambahkan data dengan union, membuat tabel berpartisi tanggal, serta menggunakan JSON, array, dan struct di BigQuery.

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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.

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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.

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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 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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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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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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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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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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