Architect Your LLM Twin
Hosted by Paul Iusztin
What you'll learn
LLM System design of your LLM Twin
→ Using the 3-pipeline architecture & MLOps good practices
Design a data collection pipeline
→ data crawling, ETLs, CDC, AWS
Design a feature pipeline
→ streaming engine in Python, data ingestion for fine-tuning & RAG, vector DBs
Design a training pipeline
→ create a custom dataset, fine-tuning, model registries, experiment trackers, LLM evaluation
Design an inference pipeline
→ real-time deployment, REST API, RAG, LLM monitoring
Why this topic matters
What is your LLM Twin? It is an AI character that writes like yourself by incorporating your style and voice into an LLM.
With an LLM Twin, you can generate posts or articles that sound like yourself in the blink of an eye.
In this session, you will learn how to design, a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.
With an LLM Twin, you can generate posts or articles that sound like yourself in the blink of an eye.
In this session, you will learn how to design, a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.
You'll learn from
Paul Iusztin
Senior ML & MLOps engineer @ Metaphysic | Co-Founder @ Decoding ML
I am a senior machine learning engineer and contractor with 7+ years of experience. I design and implement modular, scalable, and production-ready ML systems for startups worldwide.
My mission is to build data-intensive AI/ML systems that innovate the world.
I am also the co-founder of Decoding ML—a weekly eLearning ecosystem on Medium and Substack for production-grade ML & MLOps content.
The LLM Twin is built with
