A complete course for productionizing your digital doppelgänger

What it does

The repo is a free, self-paced curriculum of 12 written lessons plus reference code for building an LLM Twin: an AI character that ingests your digital footprint and learns to write in your style. It walks through four Python microservices—data collection, feature streaming, fine-tuning, and inference—using tools like Bytewax, Qdrant, AWS SageMaker, and Comet ML. The end result is a Gradio interface where you can prompt a fine-tuned model that retrieves from your own vectorized content.

The interesting bit

Instead of treating RAG and fine-tuning as separate weekend projects, the course stitches them into a single change-data-capture pipeline: your social media posts trigger RabbitMQ events, stream through Bytewax, and land in a vector DB that the inference layer actually queries. The bonus lessons even refactor the RAG layer with Superlinked to cut code volume.

Key highlights

• 12 hands-on lessons covering system design, CDC, streaming feature pipelines, LoRA fine-tuning, and prompt monitoring
• Real-time ingestion via RabbitMQ and Bytewax, with vectors stored in Qdrant (and optionally Redis via Superlinked)
• Training and inference automated on AWS SageMaker, with experiment tracking through Comet ML and evaluation via Opik
• Deployed as a scalable SageMaker REST API with a Gradio UI on top
• Most tools stay on free tiers; expect to spend about $1 on OpenAI and under $10 on AWS, depending on how much you experiment

Verdict

Worth your time if you are an intermediate Python developer who wants to see how LLM, RAG, and MLOps components wire together in production. Skip it if you are looking for deep model theory or a drop-in library—this is explicitly an engineering integration course, not a research deep-dive.