Welcome to the most technically rigorous and hands-on Large Language Model (LLM) application course available today.

This isn't just another AI course – it's your gateway to mastering the art and science of deploying production-grade LLM solutions that stand out in the real world.

As part of Maven's Top-Rated Content, this course is designed for those who have already mastered the basics of RAG, cosine similarity, vector databases, and LLMs. We'll take you to the next level, focusing on practical aspects of packaging and deploying these models in real-world production environments.

For cohort members joining this intensive learning experience, here's what you get:

- 6 weeks of in-depth content

- Weekly office hours for personalized guidance

- Real-world projects and challenging assignments

- Guest Lectures by Leading AI Professionals

- Continued support post-graduation

- Lifetime access to course materials

What You'll Master: Course Highlights

Agents: Forget CrewAI or Autogen, build your won Agents from scratch. Learn what it takes to make an Agent from the ground. Contribute to Open Source community.

Advanced RAG Solutions: Dive into enterprise-level RAG architectures and learn how to build and implement semantic caching from scratch using GCP and Redis.

LLM Hosting and Deployment: Gain insights into best practices for hosting Large Language Models (LLMs) in diverse production settings, creating inference endpoints, and deploying LLMs on serverless platforms.

Continual Pre-Training and Fine-Tuning: Explore advanced techniques for continual pre-training, fine-tuning LLMs, and mitigating catastrophic forgetting. Learn how to build a data pipeline for pre-training, apply causal language modeling, and leverage scaling laws.

Model Merging and Mixture of Experts: Master techniques for merging multiple models to enhance their collective capabilities, including the Mixture of Experts (MoE) approach. Learn to use tools like mergekit for efficient model merging.

Quantization Methods: Discover techniques to reduce model size while maintaining performance, crucial for deployment in resource-constrained environments.

Inference Speed Optimization: Learn strategies to accelerate inference speeds for real-time language processing, ensuring efficient and responsive AI systems.

Responsible AI Implementation: Explore ethical AI development using guardrails like NeMo, Colang, and Llama Guard to ensure AI systems align with responsible AI principles.

Agentic RAG and Chunking Strategies: Implement advanced semantic chunking techniques and explore AI agent frameworks like AutoGen to enhance the capabilities of RAG systems.

DSPy and Knowledge Graphs: Learn to create and utilize knowledge graphs effectively, mastering DSPy as an alternative prompting approach for structured data handling and enhanced AI interaction.

Throughout the course, we will analyze state-of-the-art AI products, reverse-engineering some through Python. As a bonus, you'll have access to experimental products being developed at Traversaal.ai, my startup, allowing you to stay at the forefront of advancements in the field.

Prerequisite: You should have coding experience of building RAG Solution. Understanding of Encoders and Decoders and some knowledge of Cloud Solutions and APIs.

If you feel the need for a more foundational course, consider checking out my other offering on LLMs: Building LLM Applications (https://maven.com/boring-bot/ml-system-design).