Data Science On The Google Cloud Platform

Download or read book Data Science on the Google Cloud Platform written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2017-12-12 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines

Download or read book Data Science on the Google Cloud Platform written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2022-03-29 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

Download or read book Data Analytics with Google Cloud Platform written by Murari Ramuka and published by BPB Publications. This book was released on 2019-12-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services DESCRIPTION Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will cover all the services that are being offered by GCP, putting emphasis on Data services. This book starts with sophisticated knowledge on Cloud Computing. It also explains different types of data services/technology and machine learning algorithm/Pre-Trained API through real-business problems, which are built on the Google Cloud Platform (GCP). With some of the latest business examples and hands-on guide, this book will enable the developers entering the data analytics fields to implement an end-to-end data pipeline, using GCP Data services. Through the course of the book, you will come across multiple industry-wise use cases, like Building Datawarehouse using Big Query, a sample real-time data analytics solution on machine learning and Artificial Intelligence that helped with the business decision, by employing a variety of data science approaches on Google Cloud environment. Whether yourÊbusinessÊis at the early stage of cloud implementation in its journey or well on its way to digital transformation,ÊGoogle Cloud'sÊsolutions and technologies will always help chart a path to success. This book can be used to develop the GCP concepts in an easy way. It contains many examples showcasing the implementation of a GCP service. It enables the learning of the basic and advance concepts of Google Cloud Data Platform. This book is divided into 7 chapters and provides a detailed description of the core concepts of each of the Data services offered by Google Cloud. KEY FEATURES Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrepÊ Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for runningÊApache SparkÊandÊApache HadoopÊclusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering WHAT WILL YOU LEARN By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Data Warehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning APIÕs to support real-life business problems. Remember to practice additional examples to master these techniques. WHO IS THIS BOOK FOR This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. While no prior knowledge of Cloud Computing or related technologies is assumed, it will be helpful to have some data background and experience. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space.ÊÊ _Ê Ê Ê Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field ofÊ data analytics, can refer/use this book to master their knowledge/understanding. _Ê Ê Ê The highlight of this book is that it will start with theÊ basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences.Ê Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCPÊ 3. Data Processing in GCP with Pub/Sub and DataflowÊ 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples

Download or read book Data Science on the Google Cloud Platform written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2022-03-29 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

Download or read book Data Analytics with Google Cloud Platform written by Murari Ramuka and published by BPB Publications. This book was released on 2019-12-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services DESCRIPTION Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will cover all the services that are being offered by GCP, putting emphasis on Data services. This book starts with sophisticated knowledge on Cloud Computing. It also explains different types of data services/technology and machine learning algorithm/Pre-Trained API through real-business problems, which are built on the Google Cloud Platform (GCP). With some of the latest business examples and hands-on guide, this book will enable the developers entering the data analytics fields to implement an end-to-end data pipeline, using GCP Data services. Through the course of the book, you will come across multiple industry-wise use cases, like Building Datawarehouse using Big Query, a sample real-time data analytics solution on machine learning and Artificial Intelligence that helped with the business decision, by employing a variety of data science approaches on Google Cloud environment. Whether yourÊbusinessÊis at the early stage of cloud implementation in its journey or well on its way to digital transformation,ÊGoogle Cloud'sÊsolutions and technologies will always help chart a path to success. This book can be used to develop the GCP concepts in an easy way. It contains many examples showcasing the implementation of a GCP service. It enables the learning of the basic and advance concepts of Google Cloud Data Platform. This book is divided into 7 chapters and provides a detailed description of the core concepts of each of the Data services offered by Google Cloud. KEY FEATURES Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrepÊ Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for runningÊApache SparkÊandÊApache HadoopÊclusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering WHAT WILL YOU LEARN By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Data Warehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning APIÕs to support real-life business problems. Remember to practice additional examples to master these techniques. WHO IS THIS BOOK FOR This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. While no prior knowledge of Cloud Computing or related technologies is assumed, it will be helpful to have some data background and experience. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space.ÊÊ _Ê Ê Ê Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field ofÊ data analytics, can refer/use this book to master their knowledge/understanding. _Ê Ê Ê The highlight of this book is that it will start with theÊ basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences.Ê Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCPÊ 3. Data Processing in GCP with Pub/Sub and DataflowÊ 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples

Download or read book Data Engineering with Google Cloud Platform written by Adi Wijaya and published by Packt Publishing Ltd. This book was released on 2022-03-31 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

Download or read book Practical AI on the Google Cloud Platform written by Micheal Lanham and published by "O'Reilly Media, Inc.". This book was released on 2020-10-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI and AutoML Tables Build a simple language processor using deep learning systems Perform image recognition using CNNs, transfer learning, and GANs Use Google's Dialogflow to create chatbots and conversational AI Analyze video with automatic video indexing, face detection, and TensorFlow Hub Build a complete working AI agent application