In this Specialization, you’ll move from using Hugging Face models to creating AI experiences you can evaluate, customize, deploy, and extend. You’ll learn how to choose models from the Hugging Face Hub, interpret model cards, run inference, tokenize inputs, and fine-tune transformers with the Trainer API.
You’ll turn those models into interactive Gradio experiences, including streaming multi-turn chatbots, and deploy them to Hugging Face Spaces. Along the way, you’ll work with hardware settings, secrets, the Gradio Python client, and model evaluation so you can make practical decisions about performance, cost, and deployment readiness.
You’ll then expand into multimodal and agentic AI with CLIP, vision-language models, Whisper, Diffusers, LoRA, multimodal RAG, smolagents, and MCP. You’ll also apply safety filtering, adversarial testing, and failure-mode documentation to assess responsible deployment.
By the end of the Specialization, you will:
Evaluate and select Hugging Face models based on task fit, limitations, licensing, and model-card evidence.
Fine-tune transformer models and turn them into interactive applications using Gradio and Hugging Face Spaces.
Design multimodal workflows for image, audio, and document-based AI use cases using VLMs, Whisper, and Diffusers.
Build agentic AI workflows with smolagents and MCP while applying safety checks and deployment-readiness practices.
Applied Learning Project
In this Specialization you'll work through applied development scenarios across the Hugging Face ecosystem. You’ll choose models from the Hub using model-card evidence, prepare datasets, fine-tune a pretrained transformer with the Trainer API, evaluate results, and publish a model card. You’ll build Gradio interfaces with gr.Interface and gr.Blocks, create streaming multi-turn chatbots with gr.ChatInterface, and deploy applications to Hugging Face Spaces with hardware configuration and secrets.
You’ll also build multimodal workflows with CLIP and vision-language models for visual question answering, image captioning, and document understanding. Additional work covers Whisper audio transcription, Diffusers image generation, decisions around LoRA fine-tuning versus multimodal RAG, agentic workflows with smolagents and MCP, and responsible deployment using ShieldGemma 2, adversarial testing, and failure-mode documentation.
