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Terapkan keterampilan Anda di Konsol Google Cloud

Mauro Rivera

Menjadi anggota sejak 2023

Google DeepMind: 07 Accelerate Your Model Earned Mei 29, 2026 EDT
Google DeepMind: 05 Fine-Tune Your Model Earned Mei 29, 2026 EDT
Google DeepMind: 04 Discover The Transformer Architecture Earned Mei 29, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks Earned Mei 29, 2026 EDT
Google DeepMind: 02 Represent Your Language Data Earned Mei 28, 2026 EDT
Google DeepMind: Train A Small Language Model Earned Mei 28, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model Earned Mei 28, 2026 EDT
Pengantar Gemini Enterprise Earned Mei 27, 2026 EDT
Memahami Agen Google Cloud Earned Mei 27, 2026 EDT
Strategi untuk Pengelolaan Risiko Keamanan Cloud Earned Nov 12, 2025 EST
Pengantar Prinsip Keamanan dalam Cloud Computing Earned Nov 10, 2025 EST
Preparing for Your Professional Cloud Security Engineer Journey Earned Okt 14, 2025 EDT
Analyzing and Visualizing Data in Looker Earned Jul 23, 2025 EDT
Menyiapkan Data untuk Dasbor dan Laporan Looker Earned Jul 23, 2025 EDT
Introduction to Looker Earned Jul 22, 2025 EDT
Meningkatkan Produktivitas dengan Gemini in BigQuery Earned Des 30, 2024 EST
Membangun Mesh Data dengan Knowledge Catalog Earned Des 27, 2024 EST
Membangun Data Warehouse dengan BigQuery Earned Des 27, 2024 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Des 19, 2024 EST
Serverless Data Processing with Dataflow: Foundations Earned Des 6, 2024 EST
Build Streaming Data Pipelines on Google Cloud Earned Des 6, 2024 EST
Build Batch Data Pipelines on Google Cloud Earned Des 6, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Des 5, 2024 EST
Pengantar Data Engineering di Google Cloud Earned Des 5, 2024 EST
Preparing for your Professional Data Engineer Journey Earned Des 5, 2024 EST
Responsible AI for Digital Leaders with Google Cloud Earned Sep 24, 2024 EDT

Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.

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Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.

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In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.

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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.

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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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Complete the advanced Google DeepMind: Train A Small Language Model skill badge by completing this course to demonstrate skills in the following: formulating real-world language model research problems; building a simple tokenizer; preparing a dataset for training a transformer language model; running the training loop of a small language model. Access this lab at no-cost by signing up for the no-cost subscription. Receive 35 free credits each month!

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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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Kursus ini memperkenalkan Gemini Enterprise, platform canggih yang menggabungkan agen AI, penelusuran lingkup perusahaan, NotebookLM, dan akses data cerdas untuk menyelesaikan tantangan organisasi. Melalui contoh nyata dan eksplorasi langsung, peserta kursus akan dapat mengaitkan kemampuan Gemini Enterprise dengan kebutuhan bisnis nyata, mendeskripsikan arsitekturnya, dan menjelaskan caranya menangani akses data dan privasi di berbagai peran.

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Kursus ini memberikan gambaran komprehensif tentang platform agen Google Cloud, termasuk Vertex AI Agent Builder, Gemini Enterprise, Conversational Agents, dan Agent Development Kit. Para peserta akan memahami kemampuan unik tiap penawaran, dapat membedakan solusi optimal untuk kasus penggunaan tertentu, dan memperoleh pengetahuan dasar dalam membuat aplikasi penelusuran dan chat.

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Ini adalah kursus kedua dari lima kursus dalam Sertifikat Keamanan Siber Google Cloud. Dalam kursus ini, Anda akan mempelajari framework pengelolaan risiko cloud yang banyak digunakan, mempelajari domain keamanan, siklus proses kepatuhan, dan standar industri seperti HIPAA, NIST CSF, dan SOC. Anda akan mengembangkan keterampilan identifikasi risiko, penerapan kontrol keamanan, evaluasi kepatuhan, dan pengelolaan perlindungan data. Selain itu, Anda akan mendapatkan pengalaman langsung dengan alat Google Cloud dan multi-cloud yang spesifik untuk risiko dan kepatuhan. Kursus ini juga mencakup teknik persiapan wawancara dan lamaran kerja, yang menawarkan fondasi komprehensif untuk memahami dan secara efektif menavigasi lanskap kompleks pengelolaan risiko cloud.

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Pengantar ini adalah kursus pertama dari lima kursus dalam Sertifikat Keamanan Siber Google Cloud. Dalam kursus ini, Anda akan mempelajari dasar-dasar pengamanan cyber, termasuk siklus proses keamanan, transformasi digital, dan konsep utama cloud computing. Anda akan mengidentifikasi alat umum yang digunakan analis keamanan cloud tingkat pemula untuk mengotomatiskan tugas.

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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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Selesaikan badge keahlian pengantar Menyiapkan Data untuk Dasbor dan Laporan Looker untuk menunjukkan keterampilan dalam hal berikut: memfilter, mengurutkan, dan melakukan pivot pada data; menggabungkan hasil dari sejumlah Eksplorasi Looker; serta menggunakan fungsi dan operator untuk membangun dasbor dan laporan Looker untuk analisis dan visualisasi data.

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In this introductory course, you'll learn how Looker can help you explore, analyze, and visualize your data to drive better decisions. Through a combination of video lectures and demos, you'll discover how to connect to various data sources, build interactive dashboards, and perform effective data analysis. Whether you're a data analyst, BI analyst, data scientist or business user, this course will equip you with the foundational knowledge to start using Looker effectively, regardless of your background.

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Kursus ini mengeksplorasi Gemini in BigQuery, yakni paket fitur yang didukung AI untuk membantu alur kerja data ke AI. Paket fitur ini meliputi eksplorasi dan persiapan data, pembuatan kode dan pemecahan masalah, serta penemuan dan visualisasi alur kerja. Melalui penjelasan konseptual, kasus penggunaan praktis, dan lab interaktif, kursus ini akan membantu para praktisi data dalam meningkatkan produktivitas mereka dan mempercepat pipeline pengembangan.

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Selesaikan badge keahlian pengantar Membangun Mesh Data dengan Knowledge Catalog untuk menunjukkan keterampilan dalam hal berikut: membuat mesh data dengan Knowledge Catalog untuk memfasilitasi keamanan, tata kelola, dan penemuan data di Google Cloud. Anda akan berlatih dan menguji keterampilan Anda dalam memberikan tag pada aset, menetapkan peran IAM, dan menilai kualitas data di Knowledge Catalog.

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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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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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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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Dalam kursus ini, Anda akan belajar tentang data engineering on Google Cloud, peran dan tanggung jawab data engineer, dan bagaimana hal tersebut terhubung dengan penawaran yang disediakan oleh Google Cloud. Anda juga akan mempelajari cara untuk mengatasi tantangan terkait data engineering.

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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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This course equips learners with the essential knowledge and practical tools to develop and implement artificial intelligence (AI) responsibly. Through an exploration of ethical considerations, best practices, and governance procedures, participants will gain an understanding of how to navigate the complex landscape of AI while upholding ethical standards and minimizing potential risks.

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