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LLM Architectures and Landscape

Explore how modern Large Language Models (LLMs) are designed, built, and deployed. This advanced webinar unpacks the architecture behind Transformers, GPT models, and emerging multimodal systems. Understand the differences between proprietary, open, and open-source models, and learn how these architectures shape today’s AI ecosystem. Perfect for engineers, researchers, and learners ready to go beyond the basics.

Date : 14 December
Location : Online
Price : 499 0
Registrations Closed !
course
# Audience

What you'll learn

The CBX experience delivers outcomes that matter at scale.

Transformer Architecture

GPT Model Evolution

Multimodal Intelligence

Real-World Applications

Future of AI Systems

Training & Fine-tuning Models

Skills you'll gain

Deep Understanding of Transformer Architecture
Knowledge of GPT Design and Training Logic
Architectural Reasoning and Model Comparison
Awareness of Multimodal Model Integration
Open-Source & Proprietary Models
Application Mapping for Real-World AI Use
Career Readiness in Advanced LLM Engineering
Evaluating open vs closed models
# endless features

Packed with useful features

The CBX experience delivers outcomes that matter at scale.

In-Depth Architectural Exploration

Learn how to compare open-source and closed LLMs based on factors such as performance, cost, data privacy, customization, deployment flexibility, and long-term scalability, helping you choose the right model for different business and technical use cases.

Real-World Applications and Case Studies

Explore how large language models are used in real-world scenarios such as chatbots, content generation, data analysis, and automation. Through practical case studies, understand how different LLM architectures are applied, their benefits, limitations, and the decision-making process behind selecting the right model for each use case.

Model Training & Fine-Tuning Overview

Understand how large language models are trained, fine-tuned, and adapted for specific tasks using techniques like pretraining, supervised fine-tuning, and instruction tuning, along with the trade-offs involved in data, compute, and performance.

Deployment & Infrastructure Considerations

Learn the key factors involved in deploying LLMs, including hardware requirements, latency, cost optimization, scalability, and on-prem vs cloud deployment choices for production-ready AI systems.

# Endless features

Meet your Mentors

mentor

Aditya Chhabra

Aditya brings 8+ years of AI engineering experience from Microsoft and leading startups, specializing in ChatGPT, generative AI, and LLM deployment. He transforms complex concepts into practical skills, mentoring hundreds of Delhi NCR engineers to master production-ready AI systems. As lead instructor at CBX, Aditya focuses on hands-on projects and real-world expertise that accelerates career growth.

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# Completion certificate

Earn a Verified Certificate of Completion

The CBX experience delivers outcomes that matter at scale.

Frequently Asked Questions

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