Muni durga devi Konduru
Date d'abonnement : 2026
Ligue d'Argent
5921 points
Create a Secure Data Lake on Cloud Storage
Earned juil. 21, 2026 EDT
Model Armor: Securing AI Deployments
Earned juil. 21, 2026 EDT
Introduction to Security in the World of AI
Earned juil. 21, 2026 EDT
Create Generative AI Apps on Google Cloud
Earned juil. 21, 2026 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned juil. 20, 2026 EDT
Prepare Data for ML APIs on Google Cloud
Earned juil. 20, 2026 EDT
[DEPRECATED] AI Boost Bites: Customer insights with NotebookLM
Earned juil. 20, 2026 EDT
AI Boost Bites: Gemini Gems – Your ultimate marketing sidekick
Earned juil. 20, 2026 EDT
Create ML Models with BigQuery ML
Earned juil. 19, 2026 EDT
Machine Learning Operations (MLOps): Getting Started
Earned juil. 18, 2026 EDT
Introduction to Generative AI
Earned juil. 18, 2026 EDT
Introduction to Large Language Models
Earned juil. 18, 2026 EDT
Responsible AI for Developers: Privacy & Safety
Earned juil. 18, 2026 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned juil. 18, 2026 EDT
Build a Certification Study Guide: PMLE
Earned juil. 18, 2026 EDT
AI Boost Bites: Your Personal Feedback Agent
Earned juil. 18, 2026 EDT
Deploy and Scale AI Models with Cloud Run
Earned juil. 17, 2026 EDT
Responsible AI for Developers: Fairness & Bias
Earned juil. 16, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned juil. 16, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned juil. 16, 2026 EDT
[DEPRECATED] Gemini for end-to-end SDLC
Earned juil. 16, 2026 EDT
Complete the introductory Create a Secure Data Lake on Cloud Storage skill badge course to demonstrate skills in the following: securing and configuring a Cloud Storage bucket, using Gemini for text generation, managing IAM access control, and establishing a Knowledge Catalog lake for data governance.
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This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI apps. Explore other content in the Gemini Agent Ready (GEAR) program.
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Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application. Explore other content in the Gemini 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 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 video covers how NotebookLM can revolutionize customer insight gathering from call or chat transcripts. You'll learn to upload PDF transcripts of hundreds of conversations (even multilingual ones!) and quickly extract key themes, trending topics, and actionable insights without listening for hours. Discover how to save findings, share notebooks, and even generate interactive podcast summaries of your data.
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This video covers how to create your own Gemini Gems, advanced AI capabilities that can automate repetitive tasks and supercharge your productivity.
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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 introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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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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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 Agent Ready (GEAR) program.
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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 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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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 video covers how to build a personalized "Work with Me" agent using Gemini Gems, which helps streamline foundational feedback and makes your meetings more strategic and efficient.
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AI inference is the process of using a trained machine learning model to make predictions on new, unseen data by applying learned patterns. This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run. It is useful for those familiar with cloud-based serverless application deployment solutions, but who may not have experience with running AI inference using Google Cloud serverless products.
The course includes examples that deploys a model for AI inference with GPUs and integrates gen AI apps with data storage services.
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This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source 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 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 Gemini Enterprise Agent Platform 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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