This hands-on, code-driven guide unlocks the power of transformer models using Hugging Face's ecosystem to build and deploy robust NLP and AI applications. Whether you're a data scientist, machine learning engineer, or advanced developer, this book equips you with the practical skills to fine-tune, optimize, scale, and deploy transformer models for real-world use cases, from sentiment analysis to chatbots and beyond. What You'll Learn: The complete process of building and deploying transformer models, from data ...
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This hands-on, code-driven guide unlocks the power of transformer models using Hugging Face's ecosystem to build and deploy robust NLP and AI applications. Whether you're a data scientist, machine learning engineer, or advanced developer, this book equips you with the practical skills to fine-tune, optimize, scale, and deploy transformer models for real-world use cases, from sentiment analysis to chatbots and beyond. What You'll Learn: The complete process of building and deploying transformer models, from data preprocessing to production-ready APIs, using Hugging Face's tools. How to fine-tune models like DistilBERT for tasks such as sentiment analysis, text classification, and named entity recognition using efficient techniques like LoRA and quantization. Techniques for integrating large language models (LLMs) with APIs, web interfaces, and cloud platforms for tasks like text generation and question answering. Building interactive applications with Hugging Face Spaces and Gradio, enabling user-friendly demos for non-technical stakeholders. Containerization with Docker for portable, reproducible deployments, optimized for size and performance. Cloud deployment strategies using AWS SageMaker and Google Cloud Vertex AI for scalable, high-performance inference. Monitoring, maintenance, and autoscaling practices, including logging, versioning, and failover to ensure reliability in production. Responsible AI practices, including model cards, bias mitigation, and privacy considerations for ethical NLP deployments. Built for Practitioners: This book is designed for those ready to move beyond basic model training and build production-grade NLP systems. It's not for beginners-it's for practitioners who want to create scalable, efficient, and ethical AI applications using open-source tools and cloud platforms. Who Should Read This Book? Data scientists and machine learning engineers building NLP solutions for tasks like sentiment analysis, chatbots, or text summarization. AI developers creating enterprise-grade applications for industries such as e-commerce, customer support, or content moderation. Researchers exploring transformer optimization, deployment strategies, or responsible AI practices. Advanced programmers leveraging Hugging Face Transformers for custom NLP workflows. Teams deploying AI solutions in production environments, from startups to large organizations. Tools Covered: Hugging Face Transformers, Datasets, Spaces, and Inference Endpoints. PyTorch for model fine-tuning and optimization. Gradio and Streamlit for building interactive web interfaces. Docker and Kubernetes for containerized deployments. AWS SageMaker, Google Cloud Vertex AI, and FastAPI for cloud-based inference. Prometheus, CloudWatch, and logging tools for observability and monitoring. Ethical tools and frameworks for bias detection and responsible AI deployment. If you're ready to harness the full potential of Hugging Face Transformers to build and deploy cutting-edge NLP applications, this is your definitive guide. Packed with practical projects, step-by-step workflows, and real-world insights, it's time to transform your ideas into production-ready AI solutions. Get your copy now and start building the future of NLP with Hugging Face.
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Add this copy of Transformers with Hugging Face: A Practical Guide to to cart. $24.16, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
