John Flack
Учасник із 2025
Діамантова ліга
Кількість балів: 28134
Model Armor: Securing AI Deployments
Earned серп. 9, 2026 EDT
Google Cloud Agent Governance and Security
Earned серп. 9, 2026 EDT
Secure Enterprise AI Agents
Earned серп. 9, 2026 EDT
Google DeepMind: Train A Small Language Model
Earned серп. 8, 2026 EDT
Google DeepMind: 07 Accelerate Your Model
Earned серп. 8, 2026 EDT
Google DeepMind: 05 Fine-Tune Your Model
Earned серп. 8, 2026 EDT
Google DeepMind: 04 Discover The Transformer Architecture
Earned серп. 8, 2026 EDT
Google DeepMind : 08 Capstone: Develop Your Model for Real-World Impact
Earned серп. 8, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned серп. 8, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned серп. 8, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned серп. 8, 2026 EDT
Налаштування мережі Google Cloud
Earned серп. 5, 2025 EDT
Налаштування середовища для розробки додатка в Google Cloud
Earned серп. 4, 2025 EDT
Build Infrastructure with Terraform on Google Cloud
Earned серп. 1, 2025 EDT
Responsible AI for Digital Leaders with Google Cloud
Earned лип. 26, 2025 EDT
Налаштування Cloud Load Balancing для Compute Engine
Earned лип. 3, 2025 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned лип. 3, 2025 EDT
Getting Started with Google Kubernetes Engine
Earned черв. 30, 2025 EDT
Essential Google Cloud Infrastructure: Core Services
Earned черв. 30, 2025 EDT
Essential Google Cloud Infrastructure: Foundation
Earned черв. 10, 2025 EDT
Google Cloud Fundamentals: Core Infrastructure - Yкраїнська
Earned черв. 9, 2025 EDT
Build a Certification Study Guide: ACE Exam Prep
Earned черв. 9, 2025 EDT
This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI apps. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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As enterprise teams transition to autonomous AI agents, security evolves toward proactive, identity-centric protection. In this course, you will implement a zero-trust security architecture for Cymbal Banking's underwriting platform using the Gemini Enterprise Agent Platform. By deploying a regional Egress Agent Gateway, configuring Model Armor and Semantic Governance (SGP) safety templates, assigning cryptographic SPIFFE identities to agent runtimes, and declaring a VPC-SC perimeter in Terraform, you will protect database boundaries and manage tools with production-grade governance.
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Learn how to secure and govern AI agents on Google Cloud. This course provides a practical roadmap to securing agent memory, enforcing strict access controls with IAM and VPC Service Controls, and deploying real-time conversation guardrails to ensure safe, compliant enterprise deployments. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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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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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 complete a capstone project that brings together the technical knowledge, ethical awareness, and creative problem-solving skills that you have developed throughout the Google DeepMind: AI Research Foundations curriculum. You will apply what you have learned to a problem of your choosing. You will first plan your project to ensure that you are clear on your project aims and intended impact. You will then collect and prepare data for your model. Finally, you will implement a full fine-tuning workflow using Gemma 3 with low-rank adaptation (LoRA) and evaluate its performance. You will receive guidance, but the emphasis will be on adaptation and experimentation - skills that are essential for applied AI research.
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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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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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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування мережі Google Cloud. У ньому ви дізнаєтеся про різні способи розгортання й моніторингу додатків, зокрема навчитеся визначати ролі керування ідентифікацією і доступом, надавати або вилучати доступ до проектів, створювати мережі VPC, розгортати й відстежувати віртуальні машини Compute Engine, писати запити SQL, а також по-різному вводити додатки в дію за допомогою Kubernetes.
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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.
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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.
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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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Пройдіть вступний кваліфікаційний курс Налаштування Cloud Load Balancing для Compute Engine, щоб продемонструвати свої навички: створення й розгортання віртуальних машин у Compute Engine; налаштування мережі й розподілювачів навантаження додатків.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.
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Курс "Знайомство з Google Cloud: основна інфраструктура" охоплює важливі поняття й терміни щодо використання Google Cloud. Переглядаючи відео й виконуючи практичні завдання, слухачі ознайомляться з різними сервісами Google Cloud для обчислень і зберігання даних, а також важливими ресурсами й інструментами для керування правилами. Крім того, вони зможуть їх порівнювати.
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Learn how to use Gemini Notebook to create a personalized study guide for the Associate Cloud 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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