LLM/Model & Papers
The LLM Engineer's Handbook ๐ท 'Super proud to announce my new book'
Curiosity: How do we bridge the gap between LLM research and production deployment?
The LLM Engineerโs Handbook: Building Production-Ready LLM Applications
Curiosity: How do we bridge the gap between LLM research and production deployment? What engineering practices enable us to build reliable, scalable LLM applications?
The LLM Engineerโs Handbook represents a comprehensive guide to building production-ready LLM applications, focusing on best engineering practices, reproducible pipelines, and end-to-end deploymentโeverything currently lacking in the ecosystem.
Book Overview
Goal: Provide everything you need to know to build LLM applications, all in one comprehensive resource.
Focus Areas:
- Best engineering practices
- Reproducible pipelines
- End-to-end deployment
- Production-ready systems
LLM Twin Course
LLM Twin Course - A practical learning resource
- GitHub: https://github.com/decodingml/llm-twin-course
- Focus: LLMs, vector DBs, and LLMOps good practices
LLM Twin Architecture
graph TB
A[User Query] --> B[LLM Twin System]
B --> C[Vector Database]
B --> D[LLM Service]
B --> E[LLMOps Pipeline]
C --> C1[Embeddings]
C --> C2[Retrieval]
D --> D1[Model Inference]
D --> D2[Response Generation]
E --> E1[Monitoring]
E --> E2[Logging]
E --> E3[Evaluation]
style A fill:#e1f5ff
style B fill:#fff3cd
style C fill:#d4edda
style D fill:#f8d7da
style E fill:#e7d4f8
Key Contributors
| Contributor | Role | Contribution |
|---|---|---|
| Paul Iusztin | Co-author | LLM Twin Course creator |
| Alex Vesa | Co-author | Engineering practices expert |
Collaboration: The team has created the excellent LLM Twin Course on GitHub, an amazing resource for learning about LLMOps.
Whatโs Covered
| Topic | Description | Importance |
|---|---|---|
| Engineering Practices | Best practices for LLM development | โญโญโญ Critical |
| Reproducible Pipelines | Version control, testing, CI/CD | โญโญโญ Critical |
| End-to-End Deployment | Production deployment strategies | โญโญโญ Critical |
| Vector Databases | Embedding storage and retrieval | โญโญ High |
| LLMOps | MLOps for LLM applications | โญโญ High |
Why This Book Matters
Retrieve: The LLM ecosystem currently lacks comprehensive engineering guidance. This book fills that gap by providing:
- Practical Examples: Real-world implementations
- Best Practices: Industry-proven patterns
- Complete Workflows: From development to deployment
- Production Focus: Scalable, maintainable systems
Innovate: By following the practices in this handbook, you can build LLM applications that are:
- Reliable and maintainable
- Scalable and efficient
- Production-ready from day one
Pre-order Information
๐ Pre-order on Amazon: https://www.amazon.com/dp/1836200072
Note: Everything online is free, but pre-ordering helps support the work and increases visibility on Amazon.
Key Takeaways
Retrieve: This handbook provides comprehensive guidance on building production-ready LLM applications, covering everything from development practices to deployment strategies.
Innovate: Apply these engineering practices to create reliable, scalable LLM systems that can handle real-world production workloads.
Curiosity โ Retrieve โ Innovation: Start with curiosity about production LLM systems, retrieve knowledge from this handbook, and innovate by building robust applications that solve real problems.
10 Data Engineering architectures to crack your next interview
- Hadoop Architecture :
https://medium.com/@shubhankarmayank/hdfs-and-architecture-of-hadoop-5cfacffcdfc0
- Hive Architecture :
https://medium.com/@shubhankarmayank/hdfs-and-architecture-of-hadoop-5cfacffcdfc0
- Spark Architecture :
https://medium.com/@knoldus/introduction-to-spark-architecture-5a2a6a304bec
- Hbase Architecture :
https://tsaiprabhanj.medium.com/hbase-architecture-e46be95cc7d3
- Kafka Architecture :
- Airflow Architecture :
https://premvishnoi.medium.com/apache-airfllow-architecture-4417c5f167f0
- BigQueryโs Architecture :
https://medium.com/@vkrntkmrsngh/bigquerys-architecture-and-working-mechanism-dad5038ebc28
- Snowflake Architecture :
https://medium.com/snowflake/2024-revisiting-snowflakes-architecture-in-a-nutshell-01f0970701a6
- Databricks Architecture :
https://premvishnoi.medium.com/data-engineer-databricks-architecture-and-services-8965a02274ba
- MongoDB Architecture : https://premvishnoi.medium.com/data-engineer-databricks-architecture-and-services-8965a02274ba
๐๐ฒ๐ ๐๐ต๐ฒ ๐๐๐น๐น ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฝ๐ฟ๐ฒ๐ฝ ๐ธ๐ถ๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ ๐ต๐ฒ๐ฟ๐ฒ - https://topmate.io/shubham_wadekar/1038815

