By the end of this course, learners will be able to:

• Load and preprocess HF Hub datasets, fine-tune a pre-trained model with the Trainer API, compute evaluation metrics, and push the result to the Hub with a model card. • Build interactive AI applications using gr.Interface and gr.Blocks with multi-component layouts, conditional visibility, session state, and event listeners • Build a streaming multi-turn chatbot using gr.ChatInterface with an LLM backend and extend it with real-time inference workflows. • Deploy a Gradio app to HF Spaces, configure hardware and secrets, evaluate cost vs. performance trade-offs, and query the deployed app programmatically using the Gradio Python client. A model stuck in a notebook is a model nobody uses. Some familiarity with the HF Transformers library and pipeline API will help you hit the ground running. The course starts where most tutorials stop — with the data. Work through a realistic fine-tuning scenario: the off-the-shelf classifier isn’t cutting it for your domain, so you’ll load a dataset from the Hub, preprocess it with the right tokenization strategy, configure the Trainer API, evaluate with real metrics, and publish the result with a model card. Once you have a model that works for your domain, the next question is: how do people use it? Wrap it in a Gradio app, graduate from quick prototypes with gr.Interface to structured applications with gr.Blocks, and add streaming chatbot behavior with gr.ChatInterface — including diagnosing why a chatbot demo feels broken the day before a client presentation. Deploy everything to Hugging Face Spaces, configure ZeroGPU when the budget won’t cover dedicated hardware, and turn your app into a programmable API endpoint. By the end, you’ll have a fine-tuned model and a live, deployed application that other systems can call.