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Full Description
If you're looking to build, deploy, and scale AI solutions with confidence, Practical RHEL AI is the guide you need. Whether you're an AI developer, data scientist, or DevOps engineer, this book walks you through the entire process—from setting up your AI development environment to optimizing and securing enterprise-scale AI workloads on Red Hat Enterprise Linux.
You'll start with the essentials: installation, configuration, and leveraging powerful machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Then, you'll dive into the tools that make AI deployment seamless—GPU acceleration, containerization, and cloud integration with AWS and Azure.
Security and compliance are non-negotiable in AI, and this book makes sure you get them right. Learn how to protect your models with encryption, implement role-based access control (RBAC), and meet industry standards like GDPR and HIPAA. You'll also master AI workload monitoring with Prometheus and Grafana, troubleshoot common issues, and automate deployments with Ansible. However, theory only gets you so far—real-world applications make the difference. Through hands-on examples and case studies in healthcare, finance, and manufacturing, you'll see how RHEL AI powers innovation in the field. Plus, you'll get insights into the future of AI, including Explainable AI (XAI), Edge AI, and AI governance. With Practical RHEL AI, you're not just learning AI—you're building AI solutions that scale.
You Will:
Learn to Install and Configure RHEL AI to optimize machine learning workloads
Understand how to train and Deploy AI models using TensorFlow, PyTorch, and Scikit-learn
Explore how to Integrate and Implement GPU acceleration, cloud computing, and containerization for scalable AI solutions
Learn to Secure and Evaluate AI workloads with encryption, RBAC, and compliance best practices
· This Book is for:
AI and machine learning engineers, DevOps and system administrators, Data scientists, and IT professionals and cloud architects
