Building Products with Open Source AI
Fine-tune, deploy, and build products with Llama, Mistral, Qwen, and other open models
A career-track specialization for engineers who want to build production products on open source models instead of paying API taxes. Covers model selection, local deployment, fine-tuning (LoRA/QLoRA), quantization, inference optimization, cost analysis, and shipping a real product powered by an open model.
- Lessons: —
- Labs: —
- Projects: —
- Level: Advanced
Curriculum
- Open Source Model Landscape — Llama 3/4, Mistral, Qwen, Phi, Gemma, model selection criteria, licensing, hardware requirements
- Local Deployment & Serving — Ollama, vLLM, TGI, llama.cpp, GPU vs CPU inference, model formats (GGUF, AWQ, GPTQ)
- Fine-Tuning with LoRA & QLoRA — LoRA mechanics, QLoRA for low-VRAM, dataset preparation, training scripts, evaluation, merging adapters
- Quantization & Optimization — INT4/INT8 quantization, AWQ, GPTQ, GGUF, KV cache optimization, speculative decoding, batching
- RAG with Open Models — Embedding models, vector DBs, chunking strategies, hybrid search, reranking, evaluation
- Building the Product — API design, streaming, rate limiting, cost monitoring, user management, deployment architecture
- Cost Analysis & Scaling — API vs self-hosted cost comparison, GPU pricing, autoscaling, multi-model routing, fallback strategies
- Capstone: Ship a Product — End-to-end product build, fine-tuned model, RAG pipeline, deployed API, user testing, iteration
Skills You Will Learn
- Open Source LLMs
- LoRA Fine-Tuning
- Quantization
- vLLM
- RAG
- Inference Optimization
- Cost Analysis
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