Deploying Machine Learning Models with Hugging Face Inference Endpoints (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Deploying Machine Learning Models with Hugging Face Inference Endpoints" Unlock the full potential of machine learning in production with "Deploying Machine Learning Models with Hugging Face Inference Endpoints." This comprehensive guide walks readers through the modern MLOps landscape, focusing on the powerful Hugging Face platform and its robust ecosystem for hosting, scaling, and serving models. The book opens with foundational concepts, offering an in-depth exploration of the Hugging Face tools, libraries, and hosting solutions, and comparing them with other popular model-serving platform...
"Deploying Machine Learning Models with Hugging Face Inference Endpoints" Unlock the full potential of machine learning in production with "Deploying Machine Learning Models with Hugging Face Inference Endpoints." This comprehensive guide walks readers through the modern MLOps landscape, focusing on the powerful Hugging Face platform and its robust ecosystem for hosting, scaling, and serving models. The book opens with foundational concepts, offering an in-depth exploration of the Hugging Face tools, libraries, and hosting solutions, and comparing them with other popular model-serving platforms to illuminate their distinct advantages in real-world deployments. As the chapters progress, the reader is skillfully led through the intricacies of operationalizing the complete machine learning model lifecycle-from training and version control to reproducible packaging, secure API exposure, and thorough testing. Practical guidance is provided for preparing models for deployment, including exporting, optimizing for inference, integrating preprocessing steps, and establishing rigorous validation pipelines. Readers will master the deployment of inference endpoints, tackling key considerations in hardware provisioning, auto-scaling, CI/CD integration, and secure configuration management, all bolstered by best practices in monitoring and troubleshooting. Delving into advanced topics, the book addresses efficient request serving, robust API security, postprocessing, and consumption patterns, while offering strategies for achieving high reliability, scalability, cost optimization, and compliance with leading global standards. With forward-looking chapters on AutoML integration, edge and federated inference, and emerging trends in serverless architectures, "Deploying Machine Learning Models with Hugging Face Inference Endpoints" stands as an essential resource for practitioners, engineers, and architects aspiring to deliver high-impact, production-ready AI solutions at scale.
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