Architecture of transformer models. Language model training techniques and challenges. Understanding model parameters and their significance. Pre-training vs. fine-tuning in LLMs.

NVIDIA Certified Associate: Generative AI LLMs Certification Training Course

Course Overview

The NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum offers an in-depth exploration of artificial intelligence (AI), machine learning (ML), with a special focus on generative AI and large language models (LLMs), tailored specifically to NVIDIA’s infrastructure and operations. This course is designed for professionals who manage or work with AI technologies like business analysts, IT managers, and sales professionals. It delves into fundamental AI concepts, practical applications of LLMs, and ethical considerations, utilizing NVIDIA’s tools such as NeMo for model customization and deployment. Participants will dive into the NVIDIA AI ecosystem, learning how to pinpoint opportunities for LLM applications, grasp design considerations for generative models, and understand the significance of responsible AI practices. The course also features modules on security, compliance, and governance tailored to generative AI, preparing learners for the NVIDIA Certified Associate Generative AI LLMs certification exam. By the end of the training, attendees will have the knowledge to drive business innovation through generative AI and comprehend its strategic impact on organizational strategies.

Course Objectives

Understand the principles of generative AI, focusing on large language models (LLMs), and learn how to leverage NVIDIA’s infrastructure and tools like NeMo for model training and deployment.
Gain hands-on experience in applying LLMs to real-world scenarios, understanding how to customize, fine-tune, and scale these models for various use cases.
Develop a deep understanding of ethical considerations in generative AI, including bias mitigation, privacy protection, and responsible AI deployment with NVIDIA’s ethical AI frameworks.
Equip yourself with the knowledge and skills necessary to pass the NVIDIA Certified Associate Generative AI LLMs exam, focusing on performance optimization, security, compliance, and governance in the context of LLMs.

Who Should Attend

Those who design, develop, or maintain AI models, particularly interested in expanding their expertise into generative AI and LLMs using NVIDIA technologies.
Professionals involved in data analysis who aim to implement or enhance their workflows with generative AI capabilities for advanced data interpretation and generation.
Individuals responsible for architecting AI solutions within organizations, looking to incorporate NVIDIA’s generative AI tools for scalable and efficient AI deployment.
Executives, managers, or consultants who need to understand the strategic implications of generative AI to make informed decisions about technology adoption and innovation.

Prerequisites

Basic AI and Machine Learning Knowledge: A fundamental understanding of AI concepts, including machine learning principles and neural networks.
Programming Skills: Proficiency in programming languages commonly used in AI, such as Python, with some experience in machine learning frameworks like PyTorch or TensorFlow.
Familiarity with NVIDIA Technologies: Basic knowledge of NVIDIA’s hardware (like GPUs) and software platforms relevant to AI, including CUDA or NVIDIA NGC.
Experience with Data Handling: Practical experience in working with large datasets, data preprocessing, and understanding data pipelines for AI model training.

Course Content

Introduction to Generative AI and LLMs

Definitions and distinctions between AI, ML, and generative AI.
Overview of large language models and their impact on technology.
NVIDIA’s role in advancing generative AI.
Key applications of LLMs in industry.

Fundamentals of LLMs

Architecture of transformer models.
Language model training techniques and challenges.
Understanding model parameters and their significance.
Pre-training vs. fine-tuning in LLMs.

NVIDIA's Infrastructure for Generative AI

NVIDIA GPUs and their specifications for AI workloads.
DGX systems for high-performance AI computing.
NVIDIA AI platforms tailored for LLMs.
Performance metrics for LLM training and inference.

NVIDIA NeMo for LLM Development

Introduction to NeMo and its ecosystem.
Customizing models with NeMo frameworks.
Model optimization and scaling with NeMo.
Deployment strategies using NeMo.

Data Preparation for LLMs

Data collection strategies for LLM training.
Data cleaning and augmentation techniques.
Handling multilingual and domain-specific data.
Efficient data pipeline design for LLMs.

Model Training and Fine-Tuning

Techniques for efficient LLM training.
Fine-tuning pre-trained models for specific tasks.
Transfer learning in the context of LLMs.
Managing training resources and costs.

LLM Deployment and Inference

Strategies for deploying LLMs in production.
Optimizing inference speed with NVIDIA TensorRT.
Scaling inference for real-world applications.
Monitoring and managing deployed models.

Ethical Considerations in Generative AI

Bias detection and mitigation in LLMs.
Privacy concerns and data security.
Ethical AI frameworks from NVIDIA.
Responsible use of generative AI in society.

Security, Compliance, and Governance for LLMs

Security best practices for AI models.
Compliance with AI regulations.
Governance models for AI projects.
Incident response and model updates in production.

Future Trends and NVIDIA's Vision

Emerging trends in generative AI technology.
NVIDIA’s roadmap for advancing LLMs.
Integration of AI with emerging tech like edge computing.
Preparing for future challenges in AI ethics and performance.

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 NVIDIA LLM concepts.

Certification Preparation

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

Certification Exam

Upon completing the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum, you will be fully prepared to sit for the NVIDIA Certified Associate Generative AI LLMs exam. This certification confirms your foundational understanding of generative AI, large language models, and NVIDIA's specialized infrastructure for these technologies. It showcases your ability to recognize opportunities for LLM applications, implement ethical AI practices, and adeptly use NVIDIA's tools like NeMo for model development and deployment. Earning this certification will significantly advance your career, positioning you for roles that require strategic foresight in generative AI and leadership in leveraging NVIDIA's ecosystem for innovative AI solutions.

Enrollment

Enroll in the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum to advance your expertise in generative AI and large language models, securing a prestigious certification. This course is your guide to becoming a certified expert in utilizing NVIDIA's platforms for generative AI. For comprehensive details and to reserve your place, visit Accumentum's registration page linked below.

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Arlington VA 22209

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