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NVIDIA Certified Associate: Generative AI Multimodal Certification Training Course

Course Overview

The NVIDIA Certified Associate Generative AI Multimodal Certification Training Course with Accumentum provides a comprehensive examination of artificial intelligence (AI), machine learning (ML), with a particular emphasis on generative AI across multiple modalities, designed specifically for NVIDIA’s infrastructure and operations. This course targets professionals who handle AI technologies, including business analysts, IT managers, and sales professionals. It explores core AI concepts, practical implementations of multimodal generative models, and ethical issues, leveraging NVIDIA’s tools like NeMo and Omniverse for model creation and deployment. Participants will immerse themselves in the NVIDIA AI ecosystem, learning to identify opportunities for multimodal AI applications, understand design considerations for these models, and appreciate the importance of ethical AI practices. The program includes segments on security, compliance, and governance specifically for multimodal AI, readying learners for the NVIDIA Certified Associate Generative AI Multimodal certification exam. Upon completion, attendees will possess the expertise to foster business innovation through multimodal AI and recognize its strategic organizational impact.

Course Objectives

  • Gain a deep understanding of generative AI across various modalities (text, image, audio), and how to leverage NVIDIA’s infrastructure and tools like NeMo and Omniverse for model development.
  • Acquire practical skills in applying multimodal AI to real-world scenarios, customizing, fine-tuning, and deploying these models for diverse applications.
  • Explore ethical considerations, including bias, privacy, and fairness in multimodal AI systems, ensuring responsible AI development and use with NVIDIA’s ethical frameworks.
  • Prepare comprehensively for the NVIDIA Certified Associate Generative AI Multimodal exam, focusing on the integration, performance, security, and governance of multimodal AI solutions using NVIDIA technology.

Who Should Attend

  • Professionals focused on developing and deploying AI models who want to expand into or specialize in multimodal generative AI using NVIDIA’s platforms.
  • Individuals interested in exploring the intersection of different data modalities in AI, aiming to enhance their research or practical applications with NVIDIA’s tools.
  • Those responsible for designing AI systems within organizations, looking to incorporate NVIDIA’s technology for advanced multimodal AI solutions.
  • Professionals who need to understand the potential and implications of multimodal AI for strategic business decisions, innovation, and product development.

Prerequisites

  • Foundational Knowledge in AI/ML: Basic understanding of artificial intelligence, machine learning concepts, including familiarity with neural networks and deep learning.
  • Programming Proficiency: Competency in programming languages like Python, with experience in working with machine learning libraries such as PyTorch or TensorFlow.
  • Understanding of NVIDIA Technologies: Prior knowledge of NVIDIA’s hardware and software solutions for AI, including CUDA, GPUs, and ideally, some exposure to NVIDIA’s AI development environments.
  • Experience with Multimodal Data: Practical experience or theoretical knowledge in handling, processing, and integrating data from multiple modalities (e.g., text, images, audio).

Course Content

Introduction to Multimodal Generative AI
  • Overview of generative AI across different modalities.
  • NVIDIA’s contributions to multimodal AI research and development.
  • Fundamental concepts and applications of multimodal AI.
  • Key distinctions between unimodal and multimodal AI models.
Fundamentals of Multimodal Learning
  • Architecture and design of multimodal neural networks.
  • Data fusion techniques for combining modalities.
  • Challenges and benefits of multimodal learning.
  • Use cases demonstrating the power of multimodal AI.
NVIDIA's Multimodal Infrastructure
  • NVIDIA hardware tailored for multimodal AI processing.
  • DGX systems for high-performance multimodal computing.
  • NVIDIA’s software ecosystem for multimodal AI.
  • Optimization strategies for multimodal training.
NVIDIA Tools for Multimodal AI
  • Introduction to NeMo for text and speech-based models.
  • Utilizing Omniverse for 3D and visual AI applications.
  • Integration of different NVIDIA tools for a unified AI platform.
  • Custom model creation and deployment with NVIDIA’s frameworks.
Data Management for Multimodal AI
  • Strategies for collecting and handling multimodal datasets.
  • Data preprocessing for different modalities.
  • Synchronization and alignment of multimodal data.
  • Efficient data pipelines for multimodal AI training.
Training Multimodal Models
  • Techniques for training models with multiple data types.
  • Fine-tuning strategies for multimodal scenarios.
  • Transfer learning across modalities.
  • Managing computational resources for large-scale training.
Multimodal Model Deployment
  • Best practices for deploying multimodal AI solutions.
  • Performance optimization for inference with NVIDIA TensorRT.
  • Real-time processing considerations for different modalities.
  • Monitoring and updating deployed multimodal models.
Ethical Considerations in Multimodal AI
  • Addressing bias in multimodal data and algorithms.
  • Privacy and security across different data types.
  • NVIDIA’s ethical AI guidelines for multimodal systems.
  • Responsible AI practices in a multimodal context.
Security, Compliance, and Governance
  • Security measures specific to multimodal AI applications.
  • Compliance with regulations for various data modalities.
  • Governance frameworks for multimodal AI projects.
  • Handling incidents and ensuring model integrity across modalities.
Future Trends and NVIDIA's Vision for Multimodal AI
  • Emerging trends in multimodal AI technology.
  • NVIDIA’s strategy for advancing multimodal AI capabilities.
  • Integration with emerging technologies like AR/VR in AI.
  • Preparing for future challenges and opportunities in multimodal AI.

Course Features

Interactive Learning

Engage with expert instructors and peers through training sessions, discussions, and practical exercises.

Comprehensive Study Materials

Access extensive resources, including e-books, video lectures, and practice exams.

Real-World Applications

Work on real-life case studies and scenarios to apply Generative AI Multimodal concepts.

Certification Preparation

Receive guidance and tips to successfully pass the NVIDIA Certified Associate Generative AI Multimodal Certification exam.

Certification Exam

Upon completing the NVIDIA Certified Associate Generative AI Multimodal Certification Training Course with Accumentum, you will be thoroughly prepared to take the NVIDIA Certified Associate Generative AI Multimodal exam. This certification verifies your foundational knowledge of generative AI across multiple modalities and NVIDIA's specialized infrastructure for these technologies. It highlights your capability to identify opportunities for multimodal AI applications, adhere to ethical AI practices, and skillfully utilize NVIDIA's tools like NeMo and Omniverse for model development and deployment. Gaining this certification will markedly enhance your career, setting you up for roles that demand strategic vision in multimodal AI and leadership in exploiting NVIDIA's ecosystem for groundbreaking AI solutions.

Enrollment

Enroll in the NVIDIA Certified Associate Generative AI Multimodal Certification Training Course with Accumentum to elevate your skills in generative AI across multiple modalities, earning a distinguished certification. This course serves as your roadmap to becoming a certified specialist in leveraging NVIDIA's platforms for multimodal AI. For full details and to secure your spot, visit Accumentum's registration page linked below.

Registration

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Suite 700

Arlington VA 22209

Copyright 2024 – Accumentum® LLC

All Rights Reserved. The certification names are trademarks of their owners.

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