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Mateusz Śliwiński

Member since 2026

Diamond League

14033 points
Organize and Govern Data with Knowledge Catalog Earned יונ 22, 2026 EDT
Build a Data Warehouse with BigQuery Earned יונ 22, 2026 EDT
Introduction to Data Analytics in Google Cloud Earned יונ 20, 2026 EDT
Gen AI Agents: Transform Your Organization Earned יונ 19, 2026 EDT
Gen AI Apps: Transform Your Work Earned יונ 19, 2026 EDT
Gen AI: Navigate the Landscape Earned יונ 19, 2026 EDT
Share Data Using Google Data Cloud Earned יונ 15, 2026 EDT
AI Services and GDC Deployments and Operations Earned יונ 15, 2026 EDT
Compute, Network, and Storage Services Configuration in GDC Earned יונ 15, 2026 EDT
GDC Platform Introduction Earned יונ 15, 2026 EDT
Quiz: GDC Air-Gapped Security Operator Fundamentals Earned יונ 15, 2026 EDT
Quiz: GDC Air-Gapped Practitioner Fundamentals Earned יונ 15, 2026 EDT
Google Cloud Fundamentals: Core Infrastructure Earned יונ 9, 2026 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned יונ 9, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned יונ 8, 2026 EDT
Gemini for end-to-end SDLC Earned יונ 8, 2026 EDT
Monitor Environments with Google Cloud Managed Service for Prometheus Earned יונ 8, 2026 EDT
Create ML Models with BigQuery ML Earned יונ 8, 2026 EDT
Manage Kubernetes in Google Cloud Earned יונ 7, 2026 EDT
Create Your First Gemini Enterprise Application Earned מאי 27, 2026 EDT
Gemini for Data Scientists and Analysts Earned מאי 26, 2026 EDT

Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.

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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 is the first of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll define the field of cloud data analysis and describe roles and responsibilities of a cloud data analyst as they relate to data acquisition, storage, processing, and visualization. You’ll explore the architecture of Google Cloud-based tools, like BigQuery and Cloud Storage, and how they are used to effectively structure, present, and report data.

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Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools.

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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.

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Gen AI: Navigate the Landscape s the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.

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Earn a skill badge by completing the Share Data Using Google Data Cloud skill badge course, where you will gain practical experience with Google Cloud Data Sharing Partners, which have proprietary datasets that customers can use for their analytics use cases. Customers subscribe to this data, query it within their own platform, then augment it with their own datasets and use their visualization tools for their customer facing dashboards.

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The course explores advanced services such as machine learning, and operational topics such as application deployment, monitoring, and troubleshooting. In addition, we’ll introduce GDC software upgrades, logging, billing, and cost monitoring.

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The course examines service resources or workload components that exist in projects. You’ll learn about Kubernetes in GDC, Artifact Registry, GDC Object Storage, Database Service, Networking, and Key Management and Security.

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This course provides an introduction to the GDC platform—which enables you to host, control, and manage infrastructure and services directly on your premises. GDC air-gapped is one component of Google Distributed Cloud offering which aligns to Google’s digital sovereignty vision. It supports public-sector customers and commercial entities that have strict data residency, security or privacy requirements.

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This quiz tests your GDC Air-Gapped Security Operator Fundamental knowledge.

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This quiz tests your GDC Air-Gapped Practitioner Fundamentals knowledge.

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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.

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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 Vertex AI 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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC). Duet AI was renamed to Gemini, our next-generation model.

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Earn a skill badge by completing the Monitor Environments with Google Cloud Managed Service for Prometheus skill badge course, where you learn Kubernetes Monitoring with Google Cloud Managed Service for Prometheus.

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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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Complete the intermediate Manage Kubernetes in Google Cloud skill badge course to demonstrate skills in the following: managing deployments with kubectl, monitoring and debugging applications on Google Kubernetes Engine (GKE), and continuous delivery techniques.

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Create your first Gemini Enterprise application to earn a skill badge! Connect diverse data sources to your application to build a powerful, unified search and analysis engine. Master advanced capabilities like deep research agents, multi-agent ideation, and NotebookLM for focused analysis.

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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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