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Apply your skills in Google Cloud console

Veilu Muthu

Учасник із 2024

Золота ліга

Кількість балів: 29779
Build Your First Agent with Agent Development Kit (ADK) Earned бер. 13, 2026 EDT
Introduction to Generative AI - Українська Earned серп. 18, 2025 EDT
Introduction to Data Analytics in Google Cloud Earned серп. 1, 2025 EDT
Preparing for your Professional Data Engineer Journey Earned вер. 22, 2024 EDT
Derive Insights from BigQuery Data Earned лип. 29, 2024 EDT
Streaming Analytics into BigQuery Earned лип. 29, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned лип. 29, 2024 EDT
Share Data Using Google Data Cloud Earned лип. 24, 2024 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned лип. 20, 2024 EDT
Networking in Google Cloud: Hands-On Practice Earned лип. 19, 2024 EDT
Implement Cloud Security Fundamentals on Google Cloud Earned лип. 19, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned лип. 16, 2024 EDT
Build a Data Warehouse with BigQuery Earned лип. 15, 2024 EDT
Build Streaming Data Pipelines on Google Cloud Earned лип. 14, 2024 EDT
The Arcade June Speedrun Earned лип. 2, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned черв. 26, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned черв. 23, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned черв. 18, 2024 EDT

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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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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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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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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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 Looker 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 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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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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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Dataproc, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.

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This course consists of a series of labs, designed to provide the learner hands-on experience performing a variety of tasks pertaining to setup and maintenance of their Google VPC networks.

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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.

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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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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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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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Race to the finish line with the Arcade June Speedrun and pick up valuable skills along with an exclusive Google Cloud Credential. Get hands-on experience with APIs, learn how to build a serverless app, and more! No prior experience needed.

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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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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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