Machine Learning with PySparkMachine Learning with PySpark



This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark.

Author: Pramod Singh

Publisher: Apress

ISBN: 9781484241318

Category:

Page: 223

View: 380

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. What You Will Learn Build a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model Who This Book Is For Data science and machine learning professionals.

Learn PySparkLearn PySpark



This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.

Author: Pramod Singh

Publisher: Apress

ISBN: 9781484249611

Category:

Page: 210

View: 211

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. What You'll Learn Develop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark Create Sequence Embeddings from Text data Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

Learning PySparkLearning PySpark



Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book Learn why and how you can efficiently use Python to process data and build machine learning models in Apache ...

Author: Tomasz Drabas

Publisher: Packt Publishing Ltd

ISBN: 9781786466259

Category:

Page: 274

View: 291

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 Develop and deploy efficient, scalable real-time Spark solutions Take your understanding of using Spark with Python to the next level with this jump start guide Who This Book Is For If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory. What You Will Learn Learn about Apache Spark and the Spark 2.0 architecture Build and interact with Spark DataFrames using Spark SQL Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively Read, transform, and understand data and use it to train machine learning models Build machine learning models with MLlib and ML Learn how to submit your applications programmatically using spark-submit Deploy locally built applications to a cluster In Detail Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications. Style and approach This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

Applied Data Science Using PySparkApplied Data Science Using PySpark



By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications.

Author: Ramcharan Kakarla

Publisher: Apress

ISBN: 1484264991

Category:

Page: 410

View: 571

Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. What You Will Learn Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations Who This Book is For Data scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.

PySpark CookbookPySpark Cookbook



What you will learn Configure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environments Create DataFrames from JSON and a dictionary using pyspark.sql Explore regression and ...

Author: Denny Lee

Publisher: Packt Publishing Ltd

ISBN: 9781788834254

Category:

Page: 330

View: 365

Combine the power of Apache Spark and Python to build effective big data applications Key Features Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data Book Description Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. What you will learn Configure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environments Create DataFrames from JSON and a dictionary using pyspark.sql Explore regression and clustering models available in the ML module Use DataFrames to transform data used for modeling Connect to PubNub and perform aggregations on streams Who this book is for The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

Hands On Big Data Analytics with PySparkHands On Big Data Analytics with PySpark



In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.

Author: Rudy Lai

Publisher: Packt Publishing Ltd

ISBN: 9781838648831

Category:

Page: 182

View: 862

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key Features Work with large amounts of agile data using distributed datasets and in-memory caching Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3 Employ the easy-to-use PySpark API to deploy big data Analytics for production Book Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learn Get practical big data experience while working on messy datasets Analyze patterns with Spark SQL to improve your business intelligence Use PySpark's interactive shell to speed up development time Create highly concurrent Spark programs by leveraging immutability Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation Re-design your jobs to use reduceByKey instead of groupBy Create robust processing pipelines by testing Apache Spark jobs Who this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.

Deploy Machine Learning Models to ProductionDeploy Machine Learning Models to Production



Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges.

Author: Pramod Singh

Publisher: Apress

ISBN: 1484265459

Category:

Page: 150

View: 690

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers

Interactive Spark Using PySparkInteractive Spark Using PySpark



Apache Spark is an in-memory framework that allows data scientists to explore and interact with big data much more quickly than with Hadoop. Python users can work with Spark using an interactive shell called PySpark. Why is it important?

Author: Benjamin Bengfort

Publisher:

ISBN: 1491965312

Category:

Page: 20

View: 171

Apache Spark is an in-memory framework that allows data scientists to explore and interact with big data much more quickly than with Hadoop. Python users can work with Spark using an interactive shell called PySpark. Why is it important? PySpark makes the large-scale data processing capabilities of Apache Spark accessible to data scientists who are more familiar with Python than Scala or Java. This also allows for reuse of a wide variety of Python libraries for machine learning, data visualization, numerical analysis, etc. What you'll learn—and how you can apply it Compare the different components provided by Spark, and what use cases they fit. Learn how to use RDDs (resilient distributed datasets) with PySpark. Write Spark applications in Python and submit them to the cluster as Spark jobs. Get an introduction to the Spark computing framework. Apply this approach to a worked example to determine the most frequent airline delays in a specific month and year. This lesson is for you because... You're a data scientist, familiar with Python coding, who needs to get up and running with PySpark You're a Python developer who needs to leverage the distributed computing resources available on a Hadoop cluster, without learning Java or Scala first Prerequisites Familiarity with writing Python applications Some familiarity with bash command-line operations Basic understanding of how to use simple functional programming constructs in Python, such as closures, lambdas, maps, etc. Materials or downloads needed in advance Apache Spark This lesson is taken from Data Analytics with Hadoop by Jenny Kim and Benjamin Bengfort.

Graph AlgorithmsGraph Algorithms



Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine ...

Author: Mark Needham

Publisher: O'Reilly Media

ISBN: 9781492047650

Category:

Page: 256

View: 748

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Learn TensorFlow 2 0Learn TensorFlow 2 0



What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who ...

Author: Pramod Singh

Publisher: Apress

ISBN: 9781484255582

Category:

Page: 164

View: 488

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.

Machine Learning with Spark and PythonMachine Learning with Spark and Python



The two classes of algorithms emphasized in the first edition continue to be
heavy favorites and are now available as part of PySpark. The beauty of this
marriage is that the code required to build machine learning models on truly
gargantuan ...

Author: Michael Bowles

Publisher: John Wiley & Sons

ISBN: 9781119561934

Category:

Page: 368

View: 795

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

Mastering Big Data Analytics with PySparkMastering Big Data Analytics with PySpark



Effectively apply Advanced Analytics to large datasets using the power of PySpark About This Video Solve your big data problems by building powerful Machine Learning models with Spark and implementing them using Python Get up-and-running ...

Author: Danny Meijer

Publisher:

ISBN: OCLC:1195920384

Category:

Page:

View: 463

Effectively apply Advanced Analytics to large datasets using the power of PySpark About This Video Solve your big data problems by building powerful Machine Learning models with Spark and implementing them using Python Get up-and-running with Spark's essential libraries and tools (such as PySpark, Spark Streaming, Spark SQL, and Spark MLlib) and learn to apply them in practical, real-world big data applications Leverage Spark 2.x - one of the most popular big data technologies-to discover how powerful Spark Machine Learning is how easily you can apply it! In Detail PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. You'll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you'll delve into various Spark components and its architecture. You'll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You'll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning. By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization.

PySpark for BeginnersPySpark for Beginners



"Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance.

Author: Tomasz Drabas

Publisher:

ISBN: OCLC:1137154350

Category:

Page:

View: 112

"Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications."--Resource description page.

Hands on PySpark for Big Data AnalysisHands on PySpark for Big Data Analysis



This is a practical, hands-on course that shows you how to use Spark and it's Python API to create performance analytics with large-scale data.

Author: Rudy Lai

Publisher:

ISBN: 1789530059

Category:

Page:

View: 343

"Data is an incredible asset, especially when there are lots of it. Exploratory data analysis, business intelligence, and machine learning all depend on processing and analyzing Big Data at scale. How do you go from working on prototypes on your local machine, to handling messy data in production and at scale? This is a practical, hands-on course that shows you how to use Spark and it's Python API to create performance analytics with large-scale data. Don't reinvent the wheel, and wow your clients by building robust and responsible applications on Big Data."--Resource description page.

Python Machine Learning BlueprintsPython Machine Learning Blueprints



This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning.

Author: Alexander Combs

Publisher: Packt Publishing Ltd

ISBN: 9781788997775

Category:

Page: 378

View: 415

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Key Features Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras Implement advanced concepts and popular machine learning algorithms in real-world projects Build analytics, computer vision, and neural network projects Book Description Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects. What you will learn Understand the Python data science stack and commonly used algorithms Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window Understand NLP concepts by creating a custom news feed Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked Gain the skills to build a chatbot from scratch using PySpark Develop a market-prediction app using stock data Delve into advanced concepts such as computer vision, neural networks, and deep learning Who this book is for This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.

Machine Learning with SparkMachine Learning with Spark



The PySpark console allows the option of setting which Python executable needs
to be used to run the shell. We can choose to use IPython, as opposed to the
standard Python shell, when launching our PySpark console. We can also pass
in ...

Author: Nick Pentreath

Publisher: Packt Publishing Ltd

ISBN: 9781783288526

Category:

Page: 338

View: 656

If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.

Learning SparkLearning Spark



This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run.

Author: Holden Karau

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449359065

Category:

Page: 276

View: 345

This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--

Data Algorithms with SparkData Algorithms with Spark



With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for ...

Author: Mahmoud Parsian

Publisher:

ISBN: OCLC:1196890897

Category:

Page: 110

View: 381

Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples for this framework using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Design machine learning algorithms including Naive Bayes, linear regression, and logistic regression Build and apply a model using PySpark design patterns Apply motif finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data (such as DNA-Seq).

Apache Spark Deep Learning CookbookApache Spark Deep Learning Cookbook



With the help of this book, you will leverage powerful deep learning libraries such as TensorFlow to develop your models and ensure their optimum performance.

Author: Ahmed Sherif

Publisher: Packt Publishing Ltd

ISBN: 9781788471558

Category:

Page: 474

View: 249

A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features Discover practical recipes for distributed deep learning with Apache Spark Learn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusable Who this book is for If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.

High Performance SparkHigh Performance Spark



With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD ...

Author: Holden Karau

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491943175

Category:

Page: 358

View: 779

Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages