According to AndrewYNg on X, deeplearning.ai launched a short course "Build and Train an LLM with JAX" in partnership with Google, taught by Chris Achard, that guides learners to implement a MiniGPT-style 20-million parameter language model using JAX, Flax/NNX, and a chat UI for inference. As reported by deeplearning.ai, the curriculum covers JAX core primitives—automatic differentiation, JIT compilation, and vectorized execution—plus constructing embeddings and transformer blocks, loading a pretrained MiniGPT checkpoint, and running chat-based inference through a graphical interface. According to AndrewYNg, JAX underpins Google’s advanced models including Gemini and Veo, positioning this course as a practical route for engineers to understand the software layer behind large model training and deployment. For businesses and developers, the course offers hands-on skills for rapid LLM prototyping on accelerators, enabling cost-aware experimentation with compact architectures, reproducible training pipelines in Flax/NNX, and production-aligned inference patterns.