Deploying AI Models with Hugging Face Inference Endpoints: A Practical Guide to Scalable Machine Learning Deployment

Unlock the full potential of production AI with *Deploying AI Models with Hugging Face Inference Endpoints: A Practical Guide to Scalable Machine Learning Deployment*. This practical guide introduces the modern MLOps landscape through the lens of the Hugging Face ecosystem, showing how to train, version, package, and serve models reliably in real-world environments. It compares Hugging Face's hosting and deployment options with other popular serving platforms, helping readers understand where each approach excels and how to choose the right solution for their workloads. As the book progresses, readers are guided through the full deployment lifecycle, from preparing models for inference to exposing secure APIs and building robust validation and testing pipelines. Clear, hands-on guidance covers exporting and optimizing models, integrating preprocessing and postprocessing steps, managing configuration securely, and setting up continuous integration and delivery workflows. The result is a practical blueprint for turning machine learning experiments into dependable services that are ready for production use. Advanced chapters explore scalable request serving, endpoint tuning, monitoring, troubleshooting, and cost-effective infrastructure choices, including auto-scaling and hardware provisioning strategies. The book also addresses essential production concerns such as security, compliance, reliability, and operational resilience, while looking ahead to topics like edge inference, federated deployment, AutoML integration, and serverless architectures. Designed for practitioners, engineers, and architects, this guide provides the knowledge needed to deploy high-impact AI systems with confidence at scale.

Pages: 228, Paperback, Independently published