Create Generative AI Apps on Google Cloud
Build Generative AI Apps with Vertex AI: From Prompt Design to RAG
- Master prompt engineering
- Improve model responses and user interactions
- Implement RAG architectures
- Enhance accuracy and relevance in AI solutions
- Align with industry
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
"Create Generative AI Apps on Google Cloud" is an immersive course designed for developers and AI enthusiasts aiming to harness the power of generative AI and large language models (LLMs) to build innovative applications. Through hands-on labs and guided modules, learners explore prompt design, retrieval-augmented generation (RAG), and Google Cloud’s Vertex AI Studio to prototype and deploy AI-driven solutions. The course covers real-world use cases, including building a RAG-based chat application, grounding LLMs with external knowledge, and optimizing model responses. By the end, participants will master production-ready architectures for generative AI and earn a skill badge validating their expertise in cutting-edge AI development.
Google's Build and Deploy Machine Learning Solutions on Vertex AI course Outline
Learn the Build and Deploy Machine Learning Solutions on Vertex AI
Ready to revolutionize app development with generative AI? Explore Create Generative AI Apps on Google Cloud on Google Cloud Skills Boost. This course teaches you to design prompts, implement RAG architectures, and build an LLM-powered chat app using Vertex AI Studio. Whether you're a developer or AI innovator, you'll gain hands-on experience with Google's latest tools. Click the link to start creating intelligent, engaging applications that redefine user experiences!
Start your journey today:
Course Structure Includes:
- Generative AI Applications (5 Links, 1 Quiz): Overview of use cases, Google's foundation models, and challenges.
- Prompt Design (5 Links, 1 Quiz): Techniques to craft effective prompts for better model outputs.
- Vertex AI Studio Lab: Prototype multimodal Gemini models and generate conversations.
- Retrieval-Augmented Generation (RAG) (6 Links, 1 Quiz): Architectures for grounding LLMs with external data.
- Build a Chat Application Lab: Create an LLM and RAG-based chatbot using Google Cloud tools.
- Course Resources: Access lesson PDFs for future reference.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn to create generative AI apps on Google Cloud. Master prompt engineering, RAG architectures, and Vertex AI Studio while building an LLM-powered chat application in hands-on labs.
Pre-requisites
Programming experience is recommended. Basic proficiency with command-line tools and Linux operating system environments is helpful. Basic understanding of Generative AI is helpful but not required.
Generative AI in Real World Workplaces PDF Free Download | SPOTO
Generative AI in Real World Workplaces PDF Free Download | SPOTO
Cours name: AI File Type: PDF
Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
Cours name: AI File Type: PDF
SPOTO Empowers You to Earn Your Certification.
Benefits of Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
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SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. Their materials likely include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on Google Cloud tools and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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