Follow along with the blog series that accompanies the full-code course from Decoding ML: Building An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin.

The architecture of the course is split into four microservices. In this course, learn how to build:

• The data collection pipeline
  • Crawl your digital data from various social media platforms.
  • Clean, normalize and load the data to a Mongo NoSQL DB through a series of ETL pipelines.
  • Send database changes to a RabbitMQ queue using the CDC pattern.
  • ☁️ Deployed on AWS.
• The feature pipeline
  • Consume messages from a queue through a Bytewax streaming pipeline.
  • Every message will be cleaned, chunked, embedded (using Superlinked, and loaded into a Qdrant vector DB in real-time.
  • ☁️ Deployed on AWS
• The training pipeline
  • Create a custom dataset based on your digital data.
  • Fine-tune an LLM using QLoRA.
  • Use Comet ML’s experiment tracker to monitor the experiments.
  • Evaluate and save the best model to Comet’s model registry.
  • ☁️ Deployed on Qwak.
• The inference pipeline
  • Load and quantize the fine-tuned LLM from Comet’s model registry.
  • Deploy it as a REST API.
  • Enhance the prompts using RAG.
  • Generate content using your LLM twin.
  • Monitor the LLM using Comet’s prompt monitoring dashboard.
  • ☁️ Deployed on Qwak.

By finishing this free course, you will learn how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.

What will you learn to build by the end of this course:

You will learn how to architect and build a real-world LLM system from start to finish - from data collection to deployment.

You will also learn to leverage MLOps best practices, such as experiment trackers, model registries, prompt monitoring, and versioning.

The end goal? Build and deploy your own LLM system.

What is an LLM Twin? It is an AI character that learns to write like somebody by incorporating its style and personality into an LLM.

Follow along with the open-source GitHub repo: https://github.com/decodingml/llm-twin-course