Practical RHEL AI

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 processfrom 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 seamlessGPU 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 farreal-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 AIyou'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

  Integrate and accelerate AI workloads using cloud services (AWS, Azure) and GPU optimization Monitor and troubleshoot AI performance with Prometheus, Grafana, and automated maintenance tools Apply and implement hands-on examples and real-world use cases in healthcare, finance, and manufacturing

  Autorentext

Luca Berton is a seasoned AI Automation and DevOps expert with more than 18 years of experience in IT, specializing in cloud infrastructure, machine learning platforms, and enterprise-scale automation. He has led major AI and automation initiatives for financial institutions such as JPMorgan Chase, Société Générale, ABN Ambro and BPCE, designing GPU-accelerated Kubernetes/OpenShift AI clusters and optimizing CI/CD pipelines for regulated environments.

Luca is the creator of the popular Ansible Pilot project and author of several best-selling technical books, including Ansible for Kubernetes by Example and Hands-On Ansible Automation . A former Red Hat engineer, he has made significant contributions to the open source ecosystem, particularly in enhancing Ansible's capabilities for cloud and AI workloads.

Widely recognized for his teaching and community leadership, Luca regularly shares his expertise through courses on Coursera, Pluralsight, and Educative, and speaks at global tech conferences on topics ranging from MLOps to infrastructure automation.

Inhalt

Chapter 1: Introduction to RHEL AI.- Chapter 2: Setting Up RHEL AI.- Chapter 3: Exploring Core Components.- Chapter 4: Advanced Features of RHEL AI.- Chapter 5: Developing Custom AI Applications.- Chapter 6: Monitoring and Maintenance.- Chapter 7: Use Cases and Best Practices.- Chapter 8: Future Trends in RHEL AI.- Chapter 9: Community and Support.