Nouveauté
The Machine Learning Engineer's Path. Build a Production-Ready Recommender System with Python
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- FormatePub
- ISBN8259604704
- EAN9798259604704
- Date de parution29/06/2026
- Protection num.pas de protection
- Taille788 Ko
- Infos supplémentairesepub
- ÉditeurChiify
Résumé
Machine learning engineer production machine learning Build, train, and deploy production models with Python, scikit-learn, and PyTorch, then ship them as live, monitored services. This practical, hands-on guide covers the entire ML lifecycle-from data preparation and model development to deployment, monitoring, and maintenance. Whether you're a beginner or a seasoned pro, you'll learn how to create robust, scalable systems that deliver real business value.
Priya Quill walks you through each stage with clear examples and best practices, ensuring you can transition from notebook experiments to production-grade applications. Topics include: setting up your environment, feature engineering, model selection, hyperparameter tuning, containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, A/B testing, logging, alerting, and performance optimization.
By the end, you'll have the skills to build and manage end-to-end machine learning systems that are reliable, maintainable, and ready for the real world. Unlike other resources that focus only on theory, this book emphasizes practical implementation and operational excellence. For readers who enjoyed [placeholder] and [placeholder], this is your next step. This hands-on AI/ML/Data 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.
Priya Quill walks you through each stage with clear examples and best practices, ensuring you can transition from notebook experiments to production-grade applications. Topics include: setting up your environment, feature engineering, model selection, hyperparameter tuning, containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, A/B testing, logging, alerting, and performance optimization.
By the end, you'll have the skills to build and manage end-to-end machine learning systems that are reliable, maintainable, and ready for the real world. Unlike other resources that focus only on theory, this book emphasizes practical implementation and operational excellence. For readers who enjoyed [placeholder] and [placeholder], this is your next step. This hands-on AI/ML/Data 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.