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Apply your skills in Google Cloud console

Venu Dadi

Member since 2024

Diamond League

10369 points
Level 1: Applied Intelligence and Cloud Architecture Earned дек. 26, 2025 EST
Google Skills Arcade Base Camp December 2025 Earned дек. 14, 2025 EST
Prepare Data for ML APIs on Google Cloud Earned дек. 14, 2025 EST
Preparing for your Professional Data Engineer Journey Earned дек. 7, 2025 EST
Build Serverless Applications with Cloud Run Functions Earned дек. 6, 2025 EST
Automate Data Capture at Scale with Document AI Earned дек. 6, 2025 EST
Store, Process, and Manage Data on Google Cloud - Command Line Earned дек. 6, 2025 EST
Streaming Analytics into BigQuery Earned дек. 5, 2025 EST
Share Data Using Google Data Cloud Earned дек. 5, 2025 EST
Build Real World AI Applications with Gemini and Imagen Earned апр. 20, 2025 EDT
Prompt Design in Vertex AI Earned апр. 20, 2025 EDT
Attention Mechanism Earned сент. 15, 2024 EDT
Introduction to Image Generation Earned сент. 15, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned сент. 15, 2024 EDT
Introduction to Responsible AI Earned сент. 12, 2024 EDT
Introduction to Large Language Models Earned сент. 12, 2024 EDT
Introduction to Generative AI Earned сент. 12, 2024 EDT

Intelligent systems today process everything from scanned invoices to massive data lakes, all while powering apps that users expect to work instantly. Handling that complexity calls for advanced AI, strong governance, and dependable cloud architecture. In this challenge, you’ll extract insights from documents with Document AI, organize and manage distributed data using Dataplex, and secure sensitive information with Cloud KMS. You’ll also deploy containerized apps on Kubernetes Engine, build real-time backends with Firebase, create no-code chat apps with AppSheet, and monitor live activity through Streamlit. These labs show how modern cloud solutions combine intelligence and architecture to stay efficient, secure, and ready for anything.

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Welcome to Base Camp, where you’ll develop key Google Cloud skills (available in Spanish and Portuguese too!) and earn an exclusive credential that will open doors to the cloud for you. No prior experience is required!

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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 Dataproc, 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 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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Earn a Introductory skill badge by completing the Build Serverless Applications with Cloud Run Functions course, where you learn how to use Cloud Run functions through the Google Cloud console and on the command line.

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Earn the introductory skill badge by completing the Automate Data Capture at Scale with Document AI course. In this course, you learn how to extract, process, and capture data using Document AI.

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Cloud Storage, Cloud Functions, and Cloud Pub/Sub are all Google Cloud Platform services that can be used to store, process, and manage data. All three services can be used together to create a variety of data-driven applications. In this skill badge you use Cloud Storage to store images, Cloud Functions to process the images, and Cloud Pub/Sub to send the images to another application.

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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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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 Build Real World AI Applications with Gemini and Imagen skill badge to demonstrate skills in the following: image recognition, natural language processing, image generation using Google's powerful Gemini and Imagen models, deploying applications on the Vertex AI platform.

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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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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 Vertex AI.

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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 3 AI principles.

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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.

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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.

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