Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI empowers developers with robust tools to construct multimodal LLM workflows that seamlessly unite text, audio, and image data within a unified pipeline. Leveraging extended context window capabilities and configurable Gemini API parameters, it facilitates the creation of sophisticated applications focused on strategic planning, complex reasoning, and cross-modal intelligence.
This instructor-led live training, available both online and onsite, is tailored for intermediate to advanced practitioners eager to design, develop, and fine-tune multimodal AI workflows on the Vertex AI platform.
Upon completion of this training, participants will be equipped to:
- Utilize Gemini models effectively for handling diverse multimodal inputs and outputs.
- Construct long-context workflows to tackle intricate reasoning tasks.
- Architect pipelines that successfully integrate text, audio, and image analysis.
- Optimize Gemini API parameters to enhance performance while maintaining cost efficiency.
Course Format
- Engaging lectures and interactive discussions.
- Practical hands-on labs focused on multimodal workflows.
- Project-based exercises designed for real-world multimodal use cases.
Customization Options
- For organizations seeking a tailored training experience, please reach out to us to discuss custom arrangements.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities within Vertex AI.
- Deep dive into Gemini models and their supported modalities.
- Exploration of relevant use cases in enterprise and research settings.
Setting Up the Development Environment
- Configuring Vertex AI specifically for multimodal workflows.
- Managing datasets across various modalities.
- Hands-on lab: Environment setup and dataset preparation.
Long Context Windows and Advanced Reasoning
- Understanding the mechanics of long-context workflows.
- Practical applications in planning and decision-making processes.
- Hands-on lab: Implementing long-context analysis.
Cross-Modal Workflow Design
- Integrating text, audio, and image analysis into cohesive solutions.
- Chaining multimodal steps within streamlined pipelines.
- Hands-on lab: Designing a comprehensive multimodal pipeline.
Working with Gemini API Parameters
- Configuring inputs and outputs for multimodal scenarios.
- Strategies for optimizing inference speed and resource efficiency.
- Hands-on lab: Tuning Gemini API parameters.
Advanced Applications and Integrations
- Developing interactive multimodal agents and virtual assistants.
- Seamless integration of external APIs and supplementary tools.
- Hands-on lab: Building a functional multimodal application.
Evaluation and Iteration
- Methods for testing multimodal performance.
- Key metrics for assessing accuracy, alignment, and data drift.
- Hands-on lab: Evaluating multimodal workflows.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Previous experience in machine learning model development.
- Familiarity with multimodal data types, including text, audio, and images.
Target Audience
- AI researchers.
- Senior software developers.
- Machine Learning scientists.
Open Training Courses require 5+ participants.
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