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Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents
Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.
In a world where computer science is now an essential element in all of our lives, a new opportunity to disseminate the latest research and trends is always welcome. This book presents the proceedings of the first International Conference on Recent Trends in Computing (ICRTC 2021), which was held as a virtual event on 21 – 22 May 2021 at Sanjivani College of Engineering, Kopargaon, India due to the restrictions of the COVID-19 pandemic. This online conference, aimed at facilitating academic exchange among researchers, enabled experts and scholars around from around the globe to gather for the discussion of the latest advanced research in the field despite the extensive travel restrictions still in place. The book contains 134 papers selected from 329 submitted papers after a rigorous peer-review process, and topics covered include advanced computing, networking, informatics, security and privacy, and other related fields. The book will be of interest to all those eager to find the latest trends and most recent developments in computer science.
Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. What You Will Learn Master the Spark unified data analytics engine and its various components Work in tandem to provide a scalable, fault tolerant and performant data processing engine Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using MLflow Who This Book Is For Data scientists, data engineers and software developers.
Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.