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Narayanan Narayanan.N

Member since 2024

Introduction to Security in the World of AI Earned أبريل 15, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned أبريل 15, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI Earned نوفمبر 8, 2024 EST
Responsible AI for Developers: Privacy & Safety Earned نوفمبر 8, 2024 EST
Responsible AI for Developers: Interpretability & Transparency Earned نوفمبر 8, 2024 EST
Responsible AI for Developers: Fairness & Bias Earned نوفمبر 8, 2024 EST
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG Earned نوفمبر 6, 2024 EST
Vector Search and Embeddings Earned نوفمبر 5, 2024 EST
The Arcade Trivia October 2024 Week 4 Earned أكتوبر 27, 2024 EDT
Level 1: Automation and CI/CD Skills Earned أكتوبر 24, 2024 EDT
Level 3: Google Cloud Adventures Earned أكتوبر 21, 2024 EDT
The Arcade Trivia October 2024 Week 3 Earned أكتوبر 21, 2024 EDT
Level 2: Cloud and Serverless Solutions Earned أكتوبر 12, 2024 EDT
The Arcade Trivia October 2024 Week 1 Earned أكتوبر 5, 2024 EDT
Introduction to Vertex AI Studio Earned يوليو 20, 2024 EDT
Create Image Captioning Models Earned يوليو 19, 2024 EDT
Transformer Models and BERT Model Earned يوليو 18, 2024 EDT
Encoder-Decoder Architecture Earned يوليو 17, 2024 EDT
Attention Mechanism Earned يوليو 16, 2024 EDT
Introduction to Image Generation Earned يوليو 16, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned يوليو 13, 2024 EDT
Prompt Design in Vertex AI Earned يوليو 12, 2024 EDT
Introduction to Responsible AI Earned مايو 16, 2024 EDT
Introduction to Large Language Models Earned مايو 16, 2024 EDT
Introduction to Generative AI Earned مايو 15, 2024 EDT

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 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 Vertex AI 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 Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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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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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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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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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.

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Hey there! You're invited to game on with the Arcade Trivia for October Week 4! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the October Trivia Week 4 badge!

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Earn your latest Google Cloud Credential by getting started with Automation and CI/CD on the Google Cloud Platform. Gain hands-on experience with Kubernetes Engine, Terraform, and Jenkins to optimize your workflows, and learn how to automatically deploy Python web apps and capture data at scale. No prior experience needed!

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Your next Google Cloud Credential is within reach! Complete a series of hands-on labs (adventures!) that will help you build key Google Cloud Skills. You’ll cover topics like Compute, Storage, Networking, IAM, Kubernetes, BigQuery, and more, working through real-world scenarios to boost your understanding. No prior experience required!

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Hey there! You're invited to game on with the Arcade Trivia for October Week 3! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the October Trivia Week 3 badge!

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Cloud services and serverless solutions have grown by over 400% in the last decade, influencing business operations and innovation. Earn your next Google Cloud Credential through hands-on labs where you'll manage Cloud SQL with Terraform, build Google Chat bots, and create no-code chat apps with AppSheet. Learn to design efficient data pipelines with Python in Dataflow and generate PDFs using Go and Cloud Run.

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Hey there! You're invited to game on with the Arcade Trivia for October Week 1! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the October Trivia Week 1 badge!

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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 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 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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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 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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