Effective machine learning (ML) now demands not just building models, but deploying and managing them at scale. Written by a seasoned senior software engineer with high-level expertise in both MLOps and LLMOps, MLOps for DevOps and Cloud Engineers equips ML practitioners, DevOps engineers, and cloud professionals with the skills to automate, monitor, and scale ML systems across environments.
The book begins with MLOps fundamentals and their roots in DevOps, exploring training workflows, model versioning, and reproducibility using pipelines. You'll implement CI/CD with GitHub Actions and Azure ML CLI, automate deployments, and manage governance and alerting for enterprise use. The author draws on their production ML experience to provide you with actionable guidance and real-world examples. A dedicated section on LLMOps covers operationalizing large language models (LLMs) like GPT-4 using RAG patterns, evaluation techniques, and responsible AI practices. You’ll also work with case studies across Azure, AWS, and GCP that offer practical context for multi-cloud operations.
Whether you're building pipelines, packaging models, or deploying LLMs, this guide delivers end-to-end strategy to build robust, scalable systems. By the end of this book, you'll be ready to design, deploy, and maintain enterprise-grade ML solutions with confidence.

Osoby które kupowały "Hands-On MLOps on Azure. Automate, Secure & Scale ML Workflows with Azure ML CLI, GitHub & LLMOps", wybierały także:

• Jak zhakowa 125,00 zł, (10,00 zł -92%)
• Power BI Desktop. Kurs video. Wykorzystanie narzędzia w analizie i wizualizacji danych 332,22 zł, (29,90 zł -91%)
• Biologika Sukcesji Pokoleniowej. Sezon 3. Konflikty na terytorium 124,17 zł, (14,90 zł -88%)
• Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
• Twórz gry w Pythonie. Kurs video. Poznaj bibliotekę PyGame 230,00 zł, (29,90 zł -87%)

Spis treści

Hands-On MLOps on Azure. Automate, secure, and scale ML workflows with the Azure ML CLI, GitHub, and LLMOps eBook -- spis treści

• 1. Understanding DevOps to MLOps
• 2. Training and Experimentation
• 3. Reproducible and Reusable ML
• 4. Model Management (Registration and Packaging)
• 5. Model Deployment: Batch Scoring and Real-Time Web Services
• 6. Capturing and Securing Governance Data for MLOps
• 7. Monitoring the ML Model
• 8. Notification and Alerting in MLOps
• 9. Automating the ML Lifecycle with ML Pipelines and GitHub Workflows
• 10. Using Models in Real-world Applications
• 11. Exploring Next-Gen MLOps