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Look inside
Build scalable, efficient, and cost-effective machine learning systems on Kubernetes.
Real-World ML Systems on Kubernetes teaches you to build scalable machine learning systems based on the industry-standard Kubernetes platform. By creating a platform-agnostic, open source system that’s an exact match for your project, you’ll sidestep vendor lock-in and inflexible off-the-shelf solutions.
In Real-World ML Systems on Kubernetes you’ll learn how to:
Real-World ML Systems on Kubernetes introduces a toolbox of open source software you can use to create custom ML platforms. In it, you’ll learn how to use Ray, Kubeflow, Airflow, Spark, JupyterHub, and Keycloak along with Kubernetes to deliver best-in-class MLOps. The book bridges the gap between theory and practice, helping you turn an academic understanding of machine learning into working models based on real-world requirements. Don’t worry—there’s no complex overviews of orchestration or cloud native development. You’ll learn just enough Kubernetes to establish and manage your ML pipeline.
Real-World ML Systems on Kubernetes teaches you to build scalable machine learning systems based on the industry-standard Kubernetes platform. By creating a platform-agnostic, open source system that’s an exact match for your project, you’ll sidestep vendor lock-in and inflexible off-the-shelf solutions.
In Real-World ML Systems on Kubernetes you’ll learn how to:
- Train machine learning models at scale
- Reliably serve machine learning models at any size
- Design and deliver a scalable data analytics platform
- Improve data science efficiency with Kubernetes
- Put modern DevOps practices into work in data science
Real-World ML Systems on Kubernetes introduces a toolbox of open source software you can use to create custom ML platforms. In it, you’ll learn how to use Ray, Kubeflow, Airflow, Spark, JupyterHub, and Keycloak along with Kubernetes to deliver best-in-class MLOps. The book bridges the gap between theory and practice, helping you turn an academic understanding of machine learning into working models based on real-world requirements. Don’t worry—there’s no complex overviews of orchestration or cloud native development. You’ll learn just enough Kubernetes to establish and manage your ML pipeline.
about the book
Real-World ML Systems on Kubernetes lays out a comprehensive blueprint for you to follow to deliver model training and deployment at scale with Kubernetes. The book is full of production-grade code samples and hands-on examples that are laser focused on utilizing Kubernetes for data science. You’ll see how Kubernetes can simplify data engineering workflows, and be utilized for advanced machine learning tasks like hyper-parameter optimization, distributed training, and reliably serving massive models.Throughout, practical projects keep you grounded in real-world applications, including developing a developer platform for data scientists with JupyterHub, running pipelines with Apache Airflow, and deploying models at scale with Ray.
about the authors
Re Alvarez Parmar is a Principal Specialist Solutions Architect at Amazon Web Services (AWS), where he advises Fortune-100 companies on building cloud-native systems. He has over 20 years of experience with architecting, building, and operating enterprise systems. He currently focuses on modern application development patterns, cloud architecture, and Kubernetes.Elamaran (Ela) Shanmugam is a Sr. Specialist Solutions Architect with Amazon Web Services with over 20 years of experience in architecting, building, and operating enterprise systems and infrastructure.
Real-World ML Systems on Kubernetes ebook for free
