Learn to build production-ready Large Language Model applications through this comprehensive course designed for developers, data scientists, and engineers. Master embeddings and vector databases for efficient search capabilities, explore multi-stage reasoning techniques including prompt engineering, LLM chains, and intelligent agents. Dive deep into fine-tuning methodologies covering few-shot learning, instruction-following models, and both service-based and DIY approaches, while understanding evaluation frameworks for LLM performance assessment. Examine the societal implications of LLMs including risks, limitations, hallucination issues, and mitigation strategies. Conclude with LLMOps practices that extend traditional MLOps to handle the unique challenges of large language model deployment and maintenance. Gain hands-on experience through extensive notebook demonstrations covering practical implementations with popular frameworks, vector database integrations including Pinecone and Weaviate, and real-world application scenarios. Features a guest lecture from Harrison Chase, creator of LangChain, providing industry insights into LLM application development. Access accompanying GitHub repository with all notebooks and slides for continued learning and reference.

Syllabus

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.6 Best Practices
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.7 Summary
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.8.1 Notebook Demo Part 1
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.8.2 Notebook Demo Part 2
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.9 Notebook Demo Pinecone (Optional)
LLM Module 2 - Embeddings, Vector Databases, and Search | 2.10 Notebook Demo Weaviate (Optional)
LLM Module 3 - Multi-stage Reasoning | 3.1 Introduction
LLM Module 3 - Multi-stage Reasoning | 3.2 Module Overview
LLM Module 3 - Multi-stage Reasoning | 3.3 Prompt Engineering
LLM Module 3 - Multi-stage Reasoning | 3.4 LLM Chains
LLM Module 3 - Multi-stage Reasoning | 3.5 Agents
LLM Module 3 - Multi-stage Reasoning | 3.6 Summary
LLM Module 3 - Multi-stage Reasoning | 3.7.1 Notebook Demo Part 1
LLM Module 3 - Multi-stage Reasoning | 3.7.2 Notebook Demo Part 2
LLM Module 3 - Multi-stage Reasoning | 3.7.3 Notebook Demo Part 3
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.1 Introduction
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.2 Module Overview
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.3 Applying Foundation LLMs
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.4 Fine Tuning: Few-shot Learning
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.5 Fine Tuning: Instruction-following LLMs
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.6 Fine Tuning: LLMs as a Service
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.7 Fine Tuning: DIY
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.8 Dolly
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.9 Evaluating LLMs
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.10 Task specific Evaluations
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.11 Guest Lecture Harrison Chase, LangChain Creator
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.12 Summary
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.13.1 Notebook Demo Part 1
LLM Module 4: Fine-tuning and Evaluating LLMs | 4.13.2 Notebook Demo Part 2
LLM Module 5: Society and LLMs | 5.1 Introduction
LLM Module 5: Society and LLMs | 5.2 Module Overview
LLM Module 5: Society and LLMs | 5.3 Risks and Limitations
LLM Module 5: Society and LLMs | 5.4 Hallucination
LLM Module 5: Society and LLMs | 5.5 Mitigation Strategies
LLM Module 5: Society and LLMs | 5.6 Summary
LLM Module 5: Society and LLMs | 5.7.1 Notebook Demo Part 1
LLM Module 5: Society and LLMs | 5.7.2 Notebook Demo Part 2
LLM Module 6: LLMOps | 6.1 Introduction
LLM Module 6: LLMOps | 6.2 Module Overview
LLM Module 6: LLMOps | 6.3 Traditional MLOps
LLM Module 6: LLMOps | 6.4 LLMOps
LLM Module 6: LLMOps | 6.5 LLMOps Details
LLM Module 6: LLMOps | 6.6 Summary
LLM Module 6: LLMOps | 6.7 Notebook Demo