Shlok Agarwal
Member since 2023
Diamond League
14266 points
Deploy Your First Agent
Earned مايو 9, 2026 EDT
Build and Deploy Agents in Production
Earned مايو 9, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned مايو 9, 2026 EDT
[DEPRECATED] Agent Summarization (Custom)
Earned سبتمبر 27, 2024 EDT
Introduction to Large Language Models
Earned سبتمبر 27, 2024 EDT
[DEPRECATED]Introduction to Responsible AI
Earned سبتمبر 27, 2024 EDT
Introduction to Generative AI
Earned سبتمبر 27, 2024 EDT
[DEPRECATED] Webhook fundamentals
Earned سبتمبر 25, 2024 EDT
[DEPRECATED] Gemini for Security Engineers
Earned سبتمبر 25, 2024 EDT
Gemini in Google Docs
Earned سبتمبر 21, 2024 EDT
Gemini in Google Drive
Earned سبتمبر 21, 2024 EDT
Gemini in Gmail
Earned سبتمبر 20, 2024 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned سبتمبر 14, 2024 EDT
Encoder-Decoder Architecture
Earned سبتمبر 13, 2024 EDT
Attention Mechanism
Earned سبتمبر 13, 2024 EDT
Introduction to Image Generation
Earned سبتمبر 13, 2024 EDT
Google Workspace with Gemini: Foundations of Your AI Workflow
Earned سبتمبر 13, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned سبتمبر 13, 2024 EDT
Take your agents from localhost to production. This course teaches you to deploy ADK agents to Agent Runtime and Cloud Run, with optional cross-session memory via Memory Bank.
Explore other content in the Gemini Agent Ready (GEAR) program.
Join the community forum for questions and discussions
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Explore the architecture and deployment of multi-agent systems using Google’s Agent Development Kit (ADK) and Google Cloud’s robust infrastructure. You will learn to design hierarchical agent trees and deterministic workflow agents while identifying the ideal hosting environment—from serverless Cloud Run to high-performance GKE—to ensure your AI agents are secure, scalable, and production-ready. Explore other content in the Gemini Agent Ready (GEAR) program.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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In this course you will learn how Contact Center AI Agent Assist can help distill complex customer interactions into concise and clear summaries.
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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 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 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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In this course, you will learn the important role that different types of webhooks play in Dialogflow CX development, and how to effectively integrate them into your routine configuration of a Virtual Agent.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you secure your cloud environment and resources. You learn how to deploy example workloads into an environment in Google Cloud, identify security misconfigurations with Gemini, and remediate security misconfigurations with Gemini. Using a hands-on lab, you experience how Gemini improves your cloud security posture.
Duet AI was renamed to Gemini, our next-generation model.
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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 Google Drive to manage and retrieve files. Learn to locate assets using natural language, extract specific facts via AI Overviews without opening files, instantly summarize multiple media formats, and save file collections as Projects for repeatable research. 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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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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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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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 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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