Boris Lublinsky – författare

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they''ve been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.

In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness.

If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You''ll explore distributed processing (the pure Python implementation of serverless) and learn how to:

Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified system for batch and stream processingApply advanced data processing with RayBuild microservices with RayImplement reliable Ray applications

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they''ve been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.

In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness.

If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You''ll explore distributed processing (the pure Python implementation of serverless) and learn how to:

Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified system for batch and stream processingApply advanced data processing with RayBuild microservices with RayImplement reliable Ray applications

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications

The go-to guidebook for deploying Big Data solutions with Hadoop

Today''s enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth.

With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them.

The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style

Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.

The go-to guidebook for deploying Big Data solutions with Hadoop

Today''s enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth.

With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them.

The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style

Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.

If you''re training a machine learning model but aren''t sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model''s lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.

Understand Kubeflow''s design, core components, and the problems it solvesUnderstand the differences between Kubeflow on different cluster typesTrain models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache SparkKeep your model up to date with Kubeflow PipelinesUnderstand how to capture model training metadataExplore how to extend Kubeflow with additional open source toolsUse hyperparameter tuning for trainingLearn how to serve your model in production

If you''re training a machine learning model but aren''t sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model''s lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.

Understand Kubeflow''s design, core components, and the problems it solvesUnderstand the differences between Kubeflow on different cluster typesTrain models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache SparkKeep your model up to date with Kubeflow PipelinesUnderstand how to capture model training metadataExplore how to extend Kubeflow with additional open source toolsUse hyperparameter tuning for trainingLearn how to serve your model in production

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solvesLearn how to set up Kubeflow on a cloud provider or on an in-house clusterTrain models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache SparkLearn how to add custom stages such as serving and predictionKeep your model up-to-date with Kubeflow PipelinesUnderstand how to validate machine learning pipelines