llmops

In this course navigates through the LLMOps pipeline, enabling you to preprocess training data for supervised fine-tuning and deploy custom Large Language Models (LLMs).

Context Runtime — a database query planner for LLM context. Decides what a model sees before it answers; plans it, runs it through reused substrate, and learns from the outcome.

Robot VLM and VLA (Vision-Language-Action) inference API helping you manage multimodal prompts, RAG, and location metadata

TokenFlow is an enterprise-grade, token-aware load balancer and AI gateway for vLLM. It prevents GPU OOM crashes using Redis-backed admission control, semantic caching, and dynamic token routing. Built in TypeScript for extreme bare-metal performance.

Companion code for the Manning book 'LLM Customization and Fine-Tuning.' Adapt open-weights LLMs end to end: prompting and RAG, LoRA/QLoRA, full SFT, distillation, and DPO/RLHF alignment.

Let's build our own scalable chatbots for the Scalable Machine Learning and Deep Learning course at KTH!!