In this live, deep learning–focused session, I will use Hugging Face on Azure Databricks to build fast, explainable NLP services with small, efficient transformers. I will select task-specific models (classification, NER, summarization), apply efficient tokenization, batching/caching and compare distilled/quantized variants for cost and latency. You’ll see how to evaluate quality with slice-aware metrics, log latency/throughput/cost to a scorecard and deploy via batch jobs or a lightweight real-time endpoint. Optional GPU paths are shown, but everything runs well on CPU. Leave with notebooks, a model selection playbook, and production-ready patterns