Practical Deep Learning at Scale with MLflow. Bridge the gap between offline experimentation and online production

Yong Liu, Dr. Matei Zaharia

The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.

Practical GIS. Learn novice to advanced topics such as QGIS, Spatial data analysis, and more

Gábor Farkas

The most commonly used GIS tools automate tasks that were historically done manually—compiling new maps by overlaying one on top of the other or physically cutting maps into pieces representing specific study areas, changing their projection, and getting meaningful results from the various layers by applying mathematical functions and operations. This book is an easy-to-follow guide to use the most matured open source GIS tools for these tasks.We’ll start by setting up the environment for the tools we use in the book. Then you will learn how to work with QGIS in order to generate useful spatial data. You will get to know the basics of queries, data management, and geoprocessing.After that, you will start to practice your knowledge on real-world examples. We will solve various types of geospatial analyses with various methods. We will start with basic GIS problems by imitating the work of an enthusiastic real estate agent, and continue with more advanced, but typical tasks by solving a decision problem. Finally, you will find out how to publish your data (and results) on the web. We will publish our data with QGIS Server and GeoServer, and create a basic web map with the API of the lightweight Leaflet web mapping library.

Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified

Atul Tripathi

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.

Practical Predictive Analytics. Analyse current and historical data to predict future trends using R, Spark, and more

Ralph Winters

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get startedwith a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio,and a description of the best practices for organizing your projects.On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement andsuccessfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn whycollaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model.We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.

Practical Real-time Data Processing and Analytics. Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

Shilpi Saxena, Saurabh Gupta

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible.This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own.We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case.By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.

Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE

Pethuru Raj Chelliah, Shreyash Naithani, Shailender Singh

Site reliability engineering (SRE) is being touted as the most competent paradigm in establishing and ensuring next-generation high-quality software solutions.This book starts by introducing you to the SRE paradigm and covers the need for highly reliable IT platforms and infrastructures. As you make your way through the next set of chapters, you will learn to develop microservices using Spring Boot and make use of RESTful frameworks. You will also learn about GitHub for deployment, containerization, and Docker containers. Practical Site Reliability Engineering teaches you to set up and sustain containerized cloud environments, and also covers architectural and design patterns and reliability implementation techniques such as reactive programming, and languages such as Ballerina and Rust. In the concluding chapters, you will get well-versed with service mesh solutions such as Istio and Linkerd, and understand service resilience test practices, API gateways, and edge/fog computing.By the end of this book, you will have gained experience on working with SRE concepts and be able to deliver highly reliable apps and services.

Practical Threat Intelligence and Data-Driven Threat Hunting. A hands-on guide to threat hunting with the ATT&CK™ Framework and open source tools

Valentina Costa-Gazcón

Threat hunting (TH) provides cybersecurity analysts and enterprises with the opportunity to proactively defend themselves by getting ahead of threats before they can cause major damage to their business.This book is not only an introduction for those who don’t know much about the cyber threat intelligence (CTI) and TH world, but also a guide for those with more advanced knowledge of other cybersecurity fields who are looking to implement a TH program from scratch.You will start by exploring what threat intelligence is and how it can be used to detect and prevent cyber threats. As you progress, you’ll learn how to collect data, along with understanding it by developing data models. The book will also show you how to set up an environment for TH using open source tools. Later, you will focus on how to plan a hunt with practical examples, before going on to explore the MITRE ATT&CK framework.By the end of this book, you’ll have the skills you need to be able to carry out effective hunts in your own environment.

Practical Time Series Analysis. Master Time Series Data Processing, Visualization, and Modeling using Python

Avishek Pal, PKS Prakash

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries

Sinan Ozdemir

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Principles of Data Science. Understand, analyze, and predict data using Machine Learning concepts and tools - Second Edition

Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas.Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.

Principles of Strategic Data Science. Creating value from data, big and small

Peter Prevos

Mathematics and computer science form an integral part of data science, and understanding them is crucial for efficiently managing data. This book is designed to take you through the entire data science pipeline and help you join the dots between mathematics, programming, and business analysis. You’ll start by learning what data science is and how organizations can use it to revolutionize the way they use their data. The book then covers the criteria for the soundness of data products and demonstrates how to effectively visualize information. As you progress, you’ll discover the strategic aspects of data science by exploring the five-phase framework that enables you to enhance the value you extract from data. Toward the concluding chapters, you’ll understand the role of a data science manager in helping an organization take the data-driven approach.By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data.

Programming PowerPoint With VBA Straight to the Point. Harness VBA to Customize, Automate, and Revolutionize PowerPoint Presentations

MrExcel's Holy Macro! Books, Eduardo N Sanchez

This book dives deep into the world of PowerPoint VBA with this practical guide. Starting with basic operations, the book moves through advanced automation techniques, covering themes, layouts, and animations. Readers will learn to craft professional-grade presentations, create interactive quizzes, and integrate PowerPoint with other Office apps, while mastering VBA essentials.This book provides a comprehensive introduction to PowerPoint VBA, enabling readers to apply programming concepts to automate repetitive tasks, enhance presentations, and build creative visual effects. With real-world projects and practical code examples, the book helps readers gain confidence in customizing PowerPoint features.Whether you're creating professional reports, interactive presentations, or seamless Office integrations, this guide ensures you stay ahead with advanced VBA techniques, enabling you to transform your PowerPoint workflow.

Puppet 3 Cookbook. An essential book if you have responsibility for servers. Real-world examples and code will give you Puppet expertise, allowing more control over servers, cloud computing, and desktops. A time-saving, career-enhancing tutorial - Second Edition

John Arundel

A revolution is happening in web operations. Configuration management tools can build servers in seconds, and automate your entire network. Tools like Puppet are essential to taking full advantage of the power of cloud computing, and building reliable, scalable, secure, high-performance systems. More and more systems administration and IT jobs require some knowledge of configuration management, and specifically Puppet.Puppet 3 Cookbook takes you beyond the basics to explore the full power of Puppet, showing you in detail how to tackle a variety of real-world problems and applications. At every step it shows you exactly what commands you need to type, and includes full code samples for every recipe.The book takes the reader from a basic knowledge of Puppet to a complete and expert understanding of Puppet's latest and most advanced features, community best practices, writing great manifests, scaling and performance, and extending Puppet by adding your own providers and resources. It starts with guidance on how to set up and expand your Puppet infrastructure, then progresses through detailed information on the language and features, external tools, reporting, monitoring, and troubleshooting, and concludes with many specific recipes for managing popular applications.The book includes real examples from production systems and techniques that are in use in some of the world's largest Puppet installations, including a distributed Puppet architecture based on the Git version control system. You'll be introduced to powerful tools that work with Puppet such as Hiera. The book also explains managing Ruby applications and MySQL databases, building web servers, load balancers, high-availability systems with Heartbeat, and many other state-of-the-art techniques

PySpark Cookbook. Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Denny Lee, Tomasz Drabas

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.

Python 3 and Data Analytics Pocket Primer. A Quick Guide to NumPy, Pandas, and Data Visualization

Mercury Learning and Information, Oswald Campesato

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.