Ashok Dwivedi
Date d'abonnement : 2025
Ligue d'Or
34000 points
Introduction to Security in the World of AI
Earned fév. 23, 2025 EST
Gemini in Google Docs
Earned fév. 18, 2025 EST
Gemini in Gmail
Earned fév. 18, 2025 EST
Google Workspace with Gemini: Foundations of Your AI Workflow
Earned fév. 18, 2025 EST
Responsible AI for Developers: Privacy & Safety
Earned fév. 18, 2025 EST
ML Pipelines on Google Cloud
Earned fév. 17, 2025 EST
Building No-Code Apps with AppSheet: Foundations
Earned fév. 14, 2025 EST
Scaling with Google Cloud Operations
Earned fév. 12, 2025 EST
Responsible AI for Developers: Interpretability & Transparency
Earned fév. 11, 2025 EST
Responsible AI for Developers: Fairness & Bias
Earned fév. 11, 2025 EST
Trust and Security with Google Cloud
Earned fév. 8, 2025 EST
Google Cloud Data Analytics Essentials
Earned fév. 7, 2025 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned fév. 7, 2025 EST
Modernize Infrastructure and Applications with Google Cloud
Earned fév. 4, 2025 EST
Innovating with Google Cloud Artificial Intelligence
Earned fév. 2, 2025 EST
Exploring Data Transformation with Google Cloud
Earned fév. 1, 2025 EST
Digital Transformation with Google Cloud
Earned fév. 1, 2025 EST
Machine Learning Operations (MLOps): Getting Started
Earned fév. 1, 2025 EST
Vector Search and Embeddings
Earned jan. 30, 2025 EST
Responsible AI: Applying AI Principles with Google Cloud
Earned jan. 30, 2025 EST
Machine Learning Operations (MLOps) for Generative AI
Earned jan. 27, 2025 EST
Implement Cloud Collaboration and Productivity Workflows
Earned jan. 26, 2025 EST
Prompt Design in Agent Platform
Earned jan. 25, 2025 EST
[DEPRECATED]Introduction to Responsible AI
Earned jan. 25, 2025 EST
Introduction to Vertex AI Studio
Earned jan. 24, 2025 EST
Introduction to Generative AI
Earned jan. 24, 2025 EST
Create Image Captioning Models
Earned jan. 24, 2025 EST
Transformer Models and BERT Model
Earned jan. 24, 2025 EST
Encoder-Decoder Architecture
Earned jan. 24, 2025 EST
Attention Mechanism
Earned jan. 23, 2025 EST
Introduction to Image Generation
Earned jan. 23, 2025 EST
Generative AI for Healthcare
Earned jan. 23, 2025 EST
Introduction to Large Language Models
Earned jan. 23, 2025 EST
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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This course covers how to use Gemini in Google Docs to streamline your writing and content creation. Learn to draft documents from scratch, refine writing tone and clarity with interactive tools, summarize complex files, and seamlessly transform long-form text into structured assets for other Google Workspace applications. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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This course covers how to use Gemini in Gmail to optimize your email management. Learn to summarize lengthy threads, retrieve information using natural language, draft professional responses, and organize communication strategies directly inside your inbox. Explore other content in the Gemini Agent Ready (GEAR) program.
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Google Workspace with Gemini provides customers with generative AI features in Google Workspace. Here, you will learn about the key features of Gemini and how they can be used to improve productivity and efficiency. Continue building your skills in the full Google Workspace with Gemini learning path, and 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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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
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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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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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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As organizations move their data and applications to the cloud, they must address a rapidly evolving landscape of security challenges. This course explores the foundations of cloud security, the value of Google Cloud’s secure-by-design infrastructure, and the defense-in-depth strategy, while highlighting how AI-driven operations and compliance tools help organizations meet strict global regulatory requirements.
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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Discover how to turn raw numbers into clear, data-driven decisions on Google Cloud. Through hands-on labs and practical real-world scenarios, this beginner-level course guides you through querying data in BigQuery, exploring trends in Looker, and crafting shareable, interactive reports in Data Studio.
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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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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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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. Innovating with Google Cloud Artificial Intelligence explores how organizations can use AI and ML to transform their business processes.
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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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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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine. Explore other content in the Gemini Agent Ready (GEAR) program.
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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 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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Earn an introductory skill badge by completing the Implement Cloud Collaboration and Productivity Workflows course, where you will get introduced to Google's collaborative platform and learn to use Gmail, Calendar, Meet, Drive, Sheets, and AppSheet.
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Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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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 course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.
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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 course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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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 Gemini Enterprise Agent Platform.
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Specifically designed for healthcare professionals, this course demystifies generative AI, the latest breakthrough in artificial intelligence, and the large language models (LLMs) that drive it. Discover real-world applications of generative AI in healthcare settings and master the art of crafting effective prompts tailored to your goals. Explore other content in the Gemini Agent Ready (GEAR) program.
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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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