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Machine Learning with Spark. Develop intelligent, distributed machine learning systems - Second Edition

Rajdeep Dua, Manpreet Singh Ghotra

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.

Machine Learning with TensorFlow 1.x. Second generation machine learning with Google's brainchild - TensorFlow 1.x

Saif Ahmed, Quan Hua, Shams Ul Azeem

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics

Rich Collier, Bahaaldine Azarmi

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.

Managing Data as a Product. Design and build data-product-centered socio-technical architectures

Andrea Gioia, Giulio Scotti

Traditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. It also covers data modeling in distributed environments and its role in enabling modern generative AI.By the end of this book, you’ll understand product-centric data architecture and how to adopt it.*Email sign-up and proof of purchase required

Managing Data Integrity for Finance. Discover practical data quality management strategies for finance analysts and data professionals

Jane Sarah Lat

Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.

Managing Data Science. Effective strategies to manage data science projects and build a sustainable team

Kirill Dubovikov

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.

Managing Microsoft Teams: MS-700 Exam Guide. Configure and manage Microsoft Teams workloads and achieve Microsoft 365 certification with ease

Peter Rising, Nate Chamberlain

Do you want to build and test your proficiency in the deployment, management, and monitoring of Microsoft Teams features within the Microsoft 365 platform? Managing Microsoft Teams: MS-700 Exam Guide will help you to effectively plan and implement Microsoft Teams using the Microsoft 365 Teams admin center and Windows PowerShell. You’ll also discover best practices for rolling out and managing MS services for Teams users within your Microsoft 365 tenant. The chapters are divided into three easy-to-follow parts: planning and design, feature policies and administration, and team management, while aligning with the official MS-700 exam objectives to help you prepare effectively for the exam.The book starts by taking you through planning and design, where you’ll learn how to plan migrations, make assessments for network readiness, and plan and implement governance tasks such as configuring guest access and monitoring usage. Later, you’ll understand feature administration, focusing on collaboration, meetings, live events, phone numbers, and the phone system, along with applicable policy configurations. Finally, the book shows you how to manage Teams and membership settings and create app policies.By the end of this book, you'll have learned everything you need to pass the MS-700 certification exam and have a handy reference guide for MS Teams.

Market Research and Analysis. Mastering Market Research: Advanced Methods, Design, and Data Analysis

Mercury Learning and Information, Marcus Goncalves

This book offers an in-depth exploration of market research and analysis, guiding readers through the entire process from defining research objectives to communicating results. Begin by understanding the purpose and ethics of market research, laying a strong groundwork for your studies. Progress to defining precise research objectives and exploring secondary research methods to gather existing information.Next, engage with primary research methods, focusing on both quantitative and qualitative approaches. Learn how to develop and distribute surveys, choose the right sampling techniques, and utilize tools for data mining and web scraping. Gain insights into focus groups and observation studies, understanding how these qualitative methods can provide depth to your research.Finally, master the art of data analysis and result communication. Explore descriptive statistics, hypothesis testing, and inferential statistics to make sense of your data. Learn to effectively present your findings to stakeholders, ensuring your research translates into actionable insights. By the end of the course, you will be well-equipped to conduct thorough market research and communicate your results effectively.