The course focuses on multimodal AI concepts, foundation models, prompt design, and enterprise use cases powered by Google Cloud’s generative AI ecosystem.
Overview
Vertex AI Multimodal LLMs Training introduces participants to building, deploying, and managing large language models that work across text, images, and other modalities using Google Vertex AI. The course focuses on multimodal AI concepts, foundation models, prompt design, and enterprise use cases powered by Google Cloud’s generative AI ecosystem.
Learning Outcomes
• Understand the fundamentals of multimodal LLMs using Vertex AI
• Learn how to process and generate text, image, audio, and video-based AI outputs
• Build and integrate multimodal AI applications using Vertex AI services and APIs
• Apply prompt engineering techniques for multimodal AI workflows and interactions
• Explore Generative AI model deployment, orchestration, and scalability concepts
• Understand responsible AI, security, and governance practices for multimodal AI systems
Duration & Delivery Mode
14 hours
Target Audience
• AI engineers and machine learning practitioners
• Cloud developers and architects
• Data scientists working with generative AI
• Enterprise AI and innovation teams
• Technical leads adopting multimodal AI
Pre-requisites
• Basic understanding of machine learning or generative AI concepts
• Familiarity with Google Cloud fundamentals
• Prior exposure to LLMs or Vertex AI is helpful but not mandatory
Skillset Achieved
• Understanding multimodal LLM concepts and architectures
• Using Vertex AI foundation and multimodal models
• Designing prompts for text, image, and multimodal inputs
• Deploying and managing multimodal LLM applications
• Applying responsible AI principles to generative models
Course Outcome
By the end of this training, participants will be able to design, deploy, and manage multimodal LLM applications using Vertex AI while following best practices for scalability, security, and responsible AI.
Course Outline
Introduction to Multimodal LLMs on Vertex AI
• Overview of multimodal large language models
• Vertex AI foundation and generative models
• Multimodal AI use cases in enterprises
Working with Text and Image Models
• Text generation and understanding using LLMs
• Image understanding and captioning
• Combining text and visual inputs
Prompt Engineering for Multimodal AI
• Prompt patterns for multimodal tasks
• Context management and grounding
• Improving output accuracy and relevance
Building Multimodal Applications
• Designing end-to-end multimodal workflows
• Integrating multimodal LLMs with applications
• API usage and orchestration
Deployment, Scaling, and Monitoring
• Deploying multimodal models on Vertex AI
• Performance optimization and cost control
• Monitoring quality and usage
Responsible AI and Governance
• Safety, bias, and ethical considerations
• Explainability and transparency
• Enterprise compliance and governance
Assessment Topics
• Fundamentals of multimodal LLMs and Vertex AI capabilities
• Multimodal data processing and AI workflow concepts
• Prompt engineering for multimodal AI applications
• AI model integration and deployment techniques
• Security, governance, and responsible AI considerations
• Practical hands-on multimodal AI implementation exercises
Evaluation
• Multimodal prompt design exercises
• Application development tasks
• Final practical assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants will receive an AcadNXT Certification in Vertex AI Multimodal LLMs Training, validating their expertise in multimodal generative AI using Google Cloud.
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What Our Students Say
“Excellent coverage of multimodal AI on Vertex.”
“The multimodal prompting techniques were very practical.”
“Helped us move from theory to real deployment.”
“Clear, structured, and highly relevant content.”
“A must-attend course for teams using Vertex AI.”