Nouveauté
Machine Learning in the Cloud. Automate and Scale Machine Learning Pipelines in the Cloud
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- FormatePub
- ISBN8259604612
- EAN9798259604612
- Date de parution29/06/2026
- Protection num.pas de protection
- Taille932 Ko
- Infos supplémentairesepub
- ÉditeurChiify
Résumé
Are you ready to deploy and scale machine learning models in the cloud with confidence? Machine Learning in the Cloud is your hands-on guide to production ML, covering managed services, pipelines, monitoring, and cloud architecture. From beginner to pro, this book teaches you to build, deploy, and monitor ML models using AWS SageMaker, Google AI Platform, and Azure ML. Learn MLOps, CI/CD for ML, model versioning, and cost optimization.
With real-world examples and step-by-step tutorials, you'll master scalable ML deployment without the hype. Whether you're a data scientist or DevOps engineer, this book bridges the gap between experimentation and production. Start your journey to cloud ML mastery today! What You'll Learn Deploy models on AWS, GCP, and Azure Automate ML pipelines with Kubeflow and Airflow Monitor model drift and retraining Optimize costs for serverless inference Implement CI/CD for machine learning This book stands out from competitors like [placeholder] and [placeholder] by focusing on practical, cloud-agnostic strategies that work in any environment.
Perfect for engineers who want to go from zero to production-ready ML systems. This hands-on Cloud/DevOps guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
With real-world examples and step-by-step tutorials, you'll master scalable ML deployment without the hype. Whether you're a data scientist or DevOps engineer, this book bridges the gap between experimentation and production. Start your journey to cloud ML mastery today! What You'll Learn Deploy models on AWS, GCP, and Azure Automate ML pipelines with Kubeflow and Airflow Monitor model drift and retraining Optimize costs for serverless inference Implement CI/CD for machine learning This book stands out from competitors like [placeholder] and [placeholder] by focusing on practical, cloud-agnostic strategies that work in any environment.
Perfect for engineers who want to go from zero to production-ready ML systems. This hands-on Cloud/DevOps guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.