Practical RHEL AI
Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux
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Erscheint vorauss. 10. Januar 2026
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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 accele...
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
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
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