Pramod Singh

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You’ll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You’ll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You’ll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark’s latest ML library. After completing this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithmsUse PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning libraryUnderstand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

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 LearnBuild, train, and deploy machine learning models at scale using KubernetesContainerize any kind of machine learning model and run it on any platform using DockerDeploy machine learning and deep learning models using Flask and Streamlit frameworksWho This Book Is ForData engineers, data scientists, analysts, and machine learning and deep learning engineers

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 LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examplesWho This Book Is ForData scientists, machine and deep learning engineers.

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 LearnDevelop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offeringsUse 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.

The quest for energy independence and rising environmental concerns are key drivers in the growing popularity of electric vehicles or EVs - electric and plug-in hybrid cars. Studies indicate that for 90% of the Americans who use their cars to get to work every day, the daily commute distance is less than 50 km - or 30 mi - and, on the average, the commuter car remains parked about 22 h per day. The EVs have in common the batteries, which provide storage capability that can be effectively harnessed when the vehicles are integrated into the grid. The entire concept of using the EVs as a distributed energy resource - load and resource - is known as the vehicle-to-grid or V2G concept. Though I have more than two decades of rendezvous with energy and diversified energy sources to quench the thirst of humanity, my specific interest in electric vehicle started in 2014 when I joined Black & Veatch and got associated with prestigious project of Tesla as strategist and adopt the success model of US market for Asia. Tesla Motors manufactures the Tesla Model S, the all-electric car that won the Motor Trend 2013 Car of the Year award. While developing the car, Tesla launched a program to aggressively deploy high-power, fast-charging stations - "Superchargers" - along major travel corridors throughout the United States. Tesla awarded Black & Veatch a contract to design and construct pilot sites in the Supercharger network. The Tesla Supercharger U.S. build-out is the largest project to date for the Black & Veatch team. Services include engineering, site assessment, and permitting and construction services for Tesla's charging stations. "It's one thing to build one Supercharger site, but it's a totally different thing to build 100 at a time, or have 40 or 50 in construction at any given time. Black & Veatch brought an ability to be able to expand rapidly, bring on the resources necessary and also manage the construction of a complex project like that - all concurrently." Kevin Kassekert, Director, Supercharger Deployment and Energy Efficiency, Tesla Motors, Inc. It was my absolute privilege to be part of the team of Black & Veatch, who is now a market leader in the design, construction and integration of complex electric vehicle (EV) and hydrogen/fuel cell vehicle (FCV) infrastructure. My journey started with a Big Bang when B&V Chairman Steve Edward pioneered the Chairman's Challenge for new and fresh ideas from offices across the global with the help of an online contest. Absolute delight was my feeling when my first idea on a strategic model of business capture ( I call it Shark Strategy) won the most voted idea of the challenge out of hundreds of ideas submitted by most of the top brains of the 10000 odd employees of the 100 year old firm. It was just the beginning as in the next Chairman's Challenge, I collaborated with others in Kansas HQ to put forth another idea on use of Drone for Industrial Application and Project Management & Monitoring of complex nature like EPC work of intercontinental pipelines or Electric Transmission Lines across the mountains or dense forest like Amazon basin. To my absolute surprise, our team won the top award of the chairman's challenge and each team members were gifted a real Drone costing not less than 15000 INR at that time, but unfortunately it could not be shipped to Mumbai for me as Drones for private applications were banned by government of India. My all other team members sent me pictures of drones awarded to them. Great Memories of Kansas City Baseball match cheering Royals after intensive strategy meetings on future of the company and American Supercharger Infrastructures ( Read Tesla, Volta and other projects). This book is my attempt to help generation next understand and support clean vehicle adoption, advance clean transportation and sustainability.