Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI equips users with robust tools to construct multimodal large language model (LLM) workflows, seamlessly integrating text, audio, and image data into a unified pipeline. Supported by extensive context window capabilities and comprehensive Gemini API parameters, this platform facilitates the creation of sophisticated applications focused on complex planning, advanced reasoning, and cross-modal intelligence.
This instructor-led, live training session (available online or on-site) is tailored for intermediate to advanced practitioners seeking to design, implement, and optimize multimodal AI workflows within the Vertex AI ecosystem.
Upon completion of this training, participants will be capable of:
- Utilizing Gemini models to process multimodal inputs and generate corresponding outputs.
- Deploying long-context workflows to handle complex reasoning tasks.
- Architecting pipelines that effectively combine text, audio, and image analysis.
- Refining Gemini API parameters to enhance both performance and cost-efficiency.
Course Delivery Format
- Engaging interactive lectures and group discussions.
- Practical, hands-on labs focused on multimodal workflows.
- Project-based exercises designed to apply multimodal concepts to real-world use cases.
Customization Options
- For organizations interested in a customized version of this course, please contact us to arrange specific requirements.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Comprehensive overview of multimodal capabilities within Vertex AI.
- Deep dive into Gemini models and their supported modalities.
- Exploration of relevant use cases in enterprise environments and research.
Setting Up the Development Environment
- Configuring Vertex AI to support multimodal workflows.
- Managing and manipulating datasets across different modalities.
- Hands-on lab: Environment configuration and dataset preparation.
Long Context Windows and Advanced Reasoning
- Understanding the mechanics of long-context workflows.
- Analyzing applications in strategic planning and decision-making processes.
- Hands-on lab: Implementing long-context analysis techniques.
Cross-Modal Workflow Design
- Integrating text, audio, and image analysis components.
- Chaining multimodal steps to create cohesive pipelines.
- Hands-on lab: Designing an end-to-end multimodal pipeline.
Working with Gemini API Parameters
- Configuring inputs and outputs for multimodal interactions.
- Strategies for optimizing inference speed and computational efficiency.
- Hands-on lab: Fine-tuning Gemini API parameters.
Advanced Applications and Integrations
- Developing interactive multimodal agents and virtual assistants.
- Integrating external APIs and auxiliary tools.
- Hands-on lab: Constructing a fully functional multimodal application.
Evaluation and Iteration
- Methods for testing and validating multimodal performance.
- Key metrics for accuracy, alignment, and detecting data drift.
- Hands-on lab: Conducting comprehensive evaluations of multimodal workflows.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Practical experience in developing machine learning models.
- Working knowledge of multimodal data types, including text, audio, and images.
Target Audience
- AI researchers
- Senior developers
- Machine learning scientists
Open Training Courses require 5+ participants.
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