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AI’nt That Easy #30: MLOps for LLM (LLMOps) vs. LLM System Design
Large Language Models (LLMs) are transforming the world, powering everything from chatbots to automated content generation. But here’s the catch: building, fine-tuning, and deploying an LLM is no easy feat. This is where MLOps (Machine Learning Operations) and LLM system design come into play.
Think of MLOps as the orchestrator of your machine learning workflows, while LLM system design ensures the entire end-to-end system (front-end, back-end, and everything in between) works seamlessly.
In this blog, we’ll take a journey through:
✅ Smart Data Preparation (because garbage in = garbage out!)
✅ Training and Fine-Tuning (reuse pipelines, save effort)
✅ Automation & Orchestration (run workflows like clockwork)
✅ Deployment (Batch vs. REST API — which one do you need?)
✅ Evaluation & Safety (because we care about responsible AI)
Let’s dive in and turn your LLM dreams into reality! 🌟
In this blog, we’ll cover:
- Data Preparation (choosing the right storage, optimization with SQL)
- Fine-tuning and Training Pipelines
- Automation and Orchestration
