Tomás Fernández
Member since 2025
Silver League
33803 points
AI Infrastructure: Networking Techniques
Earned Ağu 15, 2026 EDT
AI Infrastructure: Storage Options
Earned Ağu 15, 2026 EDT
Build Your First Agent with Agent Development Kit (ADK)
Earned Mar 12, 2026 EDT
AI Infrastructure: Deployment Types
Earned Şub 1, 2026 EST
AI Infrastructure: Cloud TPUs
Earned Oca 23, 2026 EST
AI Infrastructure: Cloud GPUs
Earned Oca 22, 2026 EST
AI Infrastructure: Introduction to AI Hypercomputer
Earned Oca 22, 2026 EST
Üretken Yapay Zeka Ajanları: Kuruluşunuzu Dönüştürün
Earned Haz 13, 2025 EDT
Üretken Yapay Zeka Uygulamaları: İşinizi Dönüştürün
Earned Haz 13, 2025 EDT
Üretken Yapay Zeka: Ekosistemi Tanıma
Earned Haz 13, 2025 EDT
Üretken Yapay Zeka: Temel Kavramları Öğrenin
Earned Haz 13, 2025 EDT
Üretken Yapay Zeka: Chatbot'tan Daha Fazlası
Earned Haz 9, 2025 EDT
Work with Gemini Models in BigQuery
Earned Nis 3, 2025 EDT
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama
Earned Mar 22, 2025 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Mar 21, 2025 EDT
Boost Productivity with Gemini in BigQuery
Earned Mar 12, 2025 EDT
Build a Data Warehouse with BigQuery
Earned Mar 11, 2025 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Mar 5, 2025 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Mar 5, 2025 EST
Build Streaming Data Pipelines on Google Cloud
Earned Şub 28, 2025 EST
Introduction to Data Engineering on Google Cloud
Earned Şub 24, 2025 EST
Build Batch Data Pipelines on Google Cloud
Earned Şub 3, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Oca 26, 2025 EST
Build a Certification Study Guide: PDE Exam Prep
Earned Oca 9, 2025 EST
Welcome to the "AI Infrastructure: Networking Techniques" course. In this course, you'll learn to leverage Google Cloud's high-bandwidth, low-latency infrastructure to optimize data transfer and communication between all the components of your AI system. By the end, you'll grasp the critical role networking plays across the entire AI pipeline from data ingestion and training to inference and be able to apply best practices to ensure your workloads run at maximum speed.
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In this course, you’ll take a comprehensive journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads. You’ll learn how to choose the right storage for each stage of the ML lifecycle. You’ll explore how to optimize for I/O performance during training, manage massive datasets for data preparation, and serve model artifacts with low latency. Through practical examples and demonstrations, you’ll gain the expertise to design robust storage solutions that accelerate your AI innovation.
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Turn your understanding of agents into practical reality by building, configuring, and running your first AI agent using Google’s Agent Development Kit (ADK).
In this hands-on course, you’ll set up a complete ADK development environment, create agents with both Python code and YAML configuration, and run them through multiple interfaces. You’ll also learn the core parameters that define agent behavior, taking what you learned in course 1 and applying it to working code.
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This course provides a comprehensive guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud. Through a series of lessons and practical demonstrations, you’ll explore diverse deployment strategies, ranging from highly customizable environments using Google Compute Engine (GCE) to managed solutions like Google Kubernetes Engine (GKE). Specifically, you’ll learn how to create clusters and deploy GKE for inference.
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Welcome to the Cloud TPUs course. We'll explore the advantages and disadvantages of TPUs in various scenarios and compare different TPU accelerators to help you choose the right fit. You'll learn strategies to maximize performance and efficiency for your AI models and understand the significance of GPU/TPU interoperability for flexible machine learning workflows. Through engaging content and practical demos, we'll guide you step-by-step in leveraging TPUs effectively.
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Curious about the powerful hardware behind AI? This module breaks down performance-optimized AI computers, showing you why they're so important. We'll explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them. By the end, you'll know exactly how to pick the right GPU for your AI projects, helping you make smart choices for your AI workloads.
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Ready to get started with AI Hypercomputer? This course makes it easy! We'll cover the basics of what they are and how they help AI with AI workloads. You'll learn about the different components inside a hypercomputer, like GPUs, TPUs, and CPUs, and discover how to pick the right deployment approach for your needs.
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Üretken Yapay Zeka Ajanları: Kuruluşunuzu Dönüştürün, Üretken Yapay Zeka Lideri öğrenme rotasının beşinci ve son kursudur. Bu kursta, kuruluşların özel üretken yapay zeka ajanlarını kullanarak belirli işletme zorluklarının üstesinden nasıl gelebileceği ele alınmaktadır. Temel bir üretken yapay zeka ajanı oluşturarak pratik yapacak, bu ajanların modeller, mantık döngüleri ve araçlar gibi bileşenlerini keşfedeceksiniz.
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Üretken Yapay Zeka Uygulamaları ile İşinizi Dönüştürün, Üretken Yapay Zeka Lideri öğrenme rotasının dördüncü kursudur. Bu kursta, Google'ın üretken yapay zeka uygulamaları (ör. Gemini ile Google Workspace ve NotebookLM) tanıtılmaktadır. Temellendirme, veriyle artırılmış üretim, etkili istemler hazırlama ve otomatik iş akışları oluşturma gibi kavramlar hakkında size rehberlik eder.
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Üretken Yapay Zeka: Ekosistemi Tanıma, Üretken Yapay Zeka Lideri öğrenme rotasının üçüncü kursudur. Üretken yapay zeka, çalışma şeklimizi ve çevremizle etkileşim kurma biçimimizi değiştiriyor. Peki bir lider olarak bu teknolojinin gücünden yararlanıp işletmenizde nasıl gerçek sonuçlar elde edebilirsiniz? Bu kursta, üretken yapay zeka çözümleri oluşturmanın farklı katmanlarını, Google Cloud'un sunduğu hizmetleri ve çözüm seçerken dikkate alınması gereken faktörleri keşfedeceksiniz.
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Üretken Yapay Zeka: Temel Kavramları Öğrenin, Üretken Yapay Zeka Lideri öğrenme rotasının ikinci kursudur. Bu kursta, yapay zeka, makine öğrenimi ve üretken yapay zeka arasındaki farkları keşfederek üretken yapay zekanın temel kavramlarını öğrenecek ve çeşitli veri türlerinin üretken yapay zekanın kurumsal zorlukları çözmesine nasıl yardımcı olduğunu anlayacaksınız. Temel modellerin sınırlamalarını gidermeye yardımcı olacak Google Cloud stratejileriyle sorumlu ve güvenli yapay zeka geliştirme ve dağıtımının temel zorlukları hakkında da bilgi edineceksiniz.
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Üretken Yapay Zeka: Chatbot'tan Daha Fazlası, Üretken Yapay Zeka Lideri öğrenme rotasının ilk kursudur ve ön koşul gerektirmez. Bu kurs, chatbot'larla ilgili temel bilgilerin ötesine geçerek üretken yapay zekanın kuruluşunuza sağlayabileceği gerçek potansiyeli keşfetmeyi amaçlamaktadır. Üretken yapay zekanın gücünden yararlanmak için çok önemli olan temel modeller ve istem mühendisliği gibi kavramları keşfedeceksiniz. Kurs ayrıca kuruluşunuz için başarılı bir üretken yapay zeka stratejisi geliştirirken dikkate almanız gereken önemli noktalar hakkında size rehberlik edecek.
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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Managed Service for Apache Spark'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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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 course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
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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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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.
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