Multimodal LLM Workflows in Vertex AI Training Course
Vertex AI offers robust tools for constructing multimodal LLM workflows that seamlessly merge text, audio, and image data into a unified pipeline. By leveraging long context window capabilities and Gemini API parameters, it empowers the development of sophisticated applications focused on planning, reasoning, and cross-modal intelligence.
This instructor-led live training, available both online and onsite, is designed for intermediate to advanced practitioners seeking to design, build, and optimize multimodal AI workflows within Vertex AI.
Upon completion of this training, participants will be able to:
- Harness Gemini models to handle multimodal inputs and outputs effectively.
- Develop long-context workflows capable of handling complex reasoning tasks.
- Architect pipelines that integrate text, audio, and image analysis.
- Tune Gemini API parameters to enhance performance while ensuring cost efficiency.
Format of the Course
- Interactive lectures and discussions.
- Practical hands-on labs featuring multimodal workflows.
- Project-based exercises applied to real-world multimodal use cases.
Course Customization Options
- For tailored training requests regarding this course, please get in touch to make arrangements.
Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Multimodal LLMs in Vertex AI
- Overview of multimodal capabilities in Vertex AI.
- Gemini models and supported modalities.
- Enterprise and research use cases.
Setting Up the Development Environment
- Configuring Vertex AI for multimodal workflows.
- Working with datasets across modalities.
- Hands-on lab: environment setup and dataset preparation.
Long Context Windows and Advanced Reasoning
- Understanding long-context workflows.
- Use cases in planning and decision-making.
- Hands-on lab: implementing long-context analysis.
Cross-Modal Workflow Design
- Combining text, audio, and image analysis.
- Chaining multimodal steps in pipelines.
- Hands-on lab: designing a multimodal pipeline.
Working with Gemini API Parameters
- Configuring multimodal inputs and outputs.
- Optimizing inference and efficiency.
- Hands-on lab: tuning Gemini API parameters.
Advanced Applications and Integrations
- Interactive multimodal agents and assistants.
- Integrating external APIs and tools.
- Hands-on lab: building a multimodal application.
Evaluation and Iteration
- Testing multimodal performance.
- Metrics for accuracy, alignment, and drift.
- Hands-on lab: evaluating multimodal workflows.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Proven experience in machine learning model development.
- Familiarity with multimodal data types, including text, audio, and images.
Audience
- AI researchers.
- Advanced software developers.
- ML scientists.
14 Hours
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Multimodal LLM Workflows in Vertex AI Training Course - Enquiry
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