Zia Ihsan
Date d'abonnement : 2026
Ligue de Diamant
12815 points
Accelerate App Development with Gemini CLI
Earned juin 17, 2026 EDT
Preparing for your Professional Data Engineer Journey
Earned juin 5, 2026 EDT
Trust and Security with Google Cloud
Earned juin 2, 2026 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned juin 2, 2026 EDT
Deploy Multi-Agent Systems with Gemini Enterprise Agent Platform
Earned juin 2, 2026 EDT
Innovating with Google Cloud Artificial Intelligence
Earned juin 2, 2026 EDT
Optimize Agent Behavior
Earned mars 11, 2026 EDT
Build Your First Agent with Agent Development Kit (ADK)
Earned mars 11, 2026 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned mars 10, 2026 EDT
Responsible AI for Developers: Privacy & Safety
Earned mars 10, 2026 EDT
[DEPRECATED] Feature Engineering
Earned mars 8, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned mars 8, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned mars 8, 2026 EDT
Introduction to Large Language Models
Earned mars 8, 2026 EDT
Introduction to Generative AI
Earned mars 8, 2026 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned mars 8, 2026 EDT
Create ML Models with BigQuery ML
Earned mars 7, 2026 EST
Working with Notebooks in Vertex AI
Earned fév. 26, 2026 EST
Prepare Data for ML APIs on Google Cloud
Earned fév. 20, 2026 EST
Agent Fundamentals
Earned fév. 13, 2026 EST
Gemini for Data Scientists and Analysts
Earned fév. 9, 2026 EST
Exploring Data Transformation with Google Cloud
Earned fév. 9, 2026 EST
Digital Transformation with Google Cloud
Earned fév. 9, 2026 EST
Build a Certification Study Guide: PMLE
Earned fév. 9, 2026 EST
Introduction to AI and Machine Learning on Google Cloud
Earned fév. 9, 2026 EST
This course is designed for app developers and DevOps engineers who want to work smarter by using Gemini CLI, a generative AI agent made for the terminal and powered by Gemini.
This course discusses Gemini CLI installation and configuration, and introduces use cases and security best practices. It explains commands, tools, MCP servers, and extensions. With a hands-on exercise, you'll install and configure Gemini CLI and use it to analyze code and build and modify an app.
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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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As organizations move their data and applications to the cloud, they must address a rapidly evolving landscape of security challenges. This course explores the foundations of cloud security, the value of Google Cloud’s secure-by-design infrastructure, and the defense-in-depth strategy, while highlighting how AI-driven operations and compliance tools help organizations meet strict global regulatory requirements.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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In this course, you’ll learn to use the Google Agent Development Kit to build complex, multi-agent systems. You will build agents equipped with tools, and connect them with parent-child relationships and flows to define how they interact. You’ll run your agents locally and deploy them to Agent Runtime on Gemini Enterprise Agent Platform to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Runtime.
Please note these labs are based off a pre-released version of this product. There may be some lag on these labs as we provide maintenance updates.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. Innovating with Google Cloud Artificial Intelligence explores how organizations can use AI and ML to transform their business processes.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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You’ve built your first agent—now it’s time to take it further. In this course, you’ll advance your skills by learning how to turn a basic AI agent into a sophisticated, precise assistant—applying advanced instructions, model selection, planning capabilities, and structured output patterns.
Join the community forum for questions and discussions
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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 introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
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This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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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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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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This course introduces the fundamentals of AI agents and explores where agents add value in the real world. It provides a foundation for developers, architects, and technical decision-makers who want to understand AI systems through the lens of autonomous, goal-directed behavior.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
Duet AI was renamed to Gemini, our next-generation model.
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Cloud technology is a powerful asset, and when paired with data, it becomes a catalyst for innovation and enhanced customer experiences. Exploring Data Transformation with Google Cloud examines how organizations can leverage the cloud to make their data more accessible, actionable, and valuable.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Digital transformation is a critical journey for modern organizations, and establishing a strong baseline in cloud computing is the first step toward driving meaningful innovation. Digital Transformation with Google Cloud introduces the core technologies and strategic frameworks that help organizations modernize their operations. This course explores fundamental cloud concepts, global network infrastructure, and the shared responsibility model to help leaders navigate their path to the cloud with confidence.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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