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Kazi Arif Hossain

Учасник із 2019

Serverless Data Processing with Dataflow: Foundations Earned вер. 9, 2026 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned серп. 25, 2026 EDT
Deploy Multi-Agent Architectures Earned серп. 24, 2026 EDT
Share Data Using Google Data Cloud Earned серп. 24, 2026 EDT
Derive Insights from BigQuery Data Earned серп. 6, 2026 EDT
Streaming Analytics into BigQuery Earned лип. 20, 2026 EDT
Build a Data Warehouse with BigQuery Earned лип. 20, 2026 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned лип. 9, 2026 EDT
Add Agent Guardrails with Callbacks Earned черв. 8, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned трав. 19, 2026 EDT
Develop Gen AI Apps with Gemini and Streamlit Earned трав. 12, 2026 EDT
Customer Experience Agent Studio: Fundamentals Earned бер. 27, 2026 EDT
Build Agents with Agent Development Kit (ADK) Earned бер. 6, 2026 EST
Optimize Agent Behavior Earned січ. 13, 2026 EST
Build Your First Agent with Agent Development Kit (ADK) Earned січ. 13, 2026 EST
Agent Fundamentals Earned січ. 11, 2026 EST
[DEPRECATED] Google Cloud: Prompt Engineering Guide Earned груд. 3, 2025 EST
[Deprecated] Virtual FAQ with data store agents Earned вер. 25, 2025 EDT
[DEPRECATED] Basic Performance Measurement Earned черв. 25, 2025 EDT
[DEPRECATED] Advanced Performance Measurement Earned черв. 24, 2025 EDT
Create Agents with Generative Playbooks Earned черв. 24, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery Earned черв. 10, 2025 EDT
Introduction to Image Generation Earned черв. 26, 2023 EDT
Generative AI Fundamentals - Українська Earned черв. 26, 2023 EDT
Introduction to Responsible AI - Українська Earned черв. 26, 2023 EDT
Introduction to Large Language Models - Українська Earned черв. 26, 2023 EDT
Introduction to Generative AI - Українська Earned черв. 25, 2023 EDT
Modernize Infrastructure and Applications with Google Cloud Earned трав. 10, 2022 EDT
Exploring Data Transformation with Google Cloud Earned трав. 10, 2022 EDT
Digital Transformation with Google Cloud Earned трав. 10, 2022 EDT

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 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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Complete the advanced Deploy Multi-Agent Architectures skill badge to demonstrate skills in the following: building multi-agent systems with ADK, connecting agents with the Agent-to-Agent (A2A) protocol, integrating external tools using the Model Context Protocol (MCP), and deploying a complete multi-agent solution to Agent Engine. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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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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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.

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Earn a skill badge by completing the Streaming Analytics into BigQuery skill badge course, where you use Pub/Sub, Dataflow and BigQuery together to stream data for analytics.

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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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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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You’ve built capable agents with powerful tools—now ensure they operate safely and reliably. Use callbacks as observation points and control mechanisms. Monitor what’s happening, validate inputs and outputs, implement guardrails, and gain confidence that your agents behave in production.

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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 Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course aims to equip conversational designers and builders with the concepts and practical skills to design, build, and test sophisticated, multi-agent, and multimodal Customer Experience agents using Customer Experience Agent Studio (CX Agent Studio).

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Learn about how you can use Agent Development Kit (ADK) to build complex, production-ready AI agents. This course covers ADK’s open-source framework, moving from simple prompt engineering to a code-first, structured software development approach suitable for enterprise-grade, multi-agent systems. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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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. Explore other content in the Gemini Agent Ready (GEAR) program. 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.Explore other content in the Gemini Enterprise Agent Ready (GEAR) program

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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. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.

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In this course, you will learn how to develop a generative agent capable of answering questions from websites, documents, and/or unstructured data.

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This course explores the fundamentals of the feedback loop process for Virtual Agent development and introduces the native capabilities within Dialogflow CX that support it

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In this course, you will learn about advanced methods and tools to monitor the performance of your virtual agents in Dialogflow CX

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This course will teach you how to build conversational experiences for Conversational Agents using Generative Playbooks. You'll start with an introduction to playbooks and learn how to set up your first one. You'll also learn about the importance of testing, as well as key production considerations like quota limits and integration. The course concludes with a case study that shows how to use playbooks for generative steering. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Gemini Enterprise Agent Platform.

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Щоб отримати кваліфікаційний значок, пройдіть курси "Introduction to Generative AI", "Introduction to Large Language Models" й "Introduction to Responsible AI". Пройшовши завершальний тест, ви підтвердите, що засвоїли основні поняття, які стосуються генеративного штучного інтелекту. Кваліфікаційний значок – це цифровий значок від платформи Google Cloud, який свідчить, що ви знаєтеся на продуктах і сервісах Google Cloud. Щоб опублікувати кваліфікаційний значок, зробіть свій профіль загальнодоступним, а також додайте значок у профіль у соціальних мережах.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.

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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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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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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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