Practical Machine Learning with Spark
Practical Machine Learning with Spark

Author: Gourav Gupta

Publisher: BPB Publications

Published: 2022-04-28

Total Pages: 501

ISBN-13: 9391392083

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Explore the cosmic secrets of Distributed Processing for Deep Learning applications KEY FEATURES ● In-depth practical demonstration of ML/DL concepts using Distributed Framework. ● Covers graphical illustrations and visual explanations for ML/DL pipelines. ● Includes live codebase for each of NLP, computer vision and machine learning applications. DESCRIPTION This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark. The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes. Towards the end of the book, readers will learn about several deep learning frameworks, such as TensorFlow and PyTorch. Readers will also learn to execute distributed processing of deep learning problems using the Spark programming language WHAT YOU WILL LEARN ●Learn how to get started with machine learning projects using Spark. ● Witness how to use Spark MLib's design for machine learning and deep learning operations. ● Use Spark in tasks involving NLP, unsupervised learning, and computer vision. ● Experiment with Spark in a cloud environment and with AI pipeline workflows. ● Run deep learning applications on a distributed network. WHO THIS BOOK IS FOR This book is valuable for data engineers, machine learning engineers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Apache Spark Environment Setup and Configuration 3. Apache Spark 4. Apache Spark MLlib 5. Supervised Learning with Spark 6. Un-Supervised Learning with Apache Spark 7. Natural Language Processing with Apache Spark 8. Recommendation Engine with Distributed Framework 9. Deep Learning with Spark 10. Computer Vision with Apache Spark

Practical Machine Learning with Rust
Practical Machine Learning with Rust

Author: Joydeep Bhattacharjee

Publisher: Apress

Published: 2019-12-10

Total Pages: 362

ISBN-13: 1484251210

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Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

PRACTICAL MACHINE LEARNING APPLICATIONS
PRACTICAL MACHINE LEARNING APPLICATIONS

Author: Dr. Sachin R. Jadhav

Publisher: Xoffencerpublication

Published: 2023-09-18

Total Pages: 204

ISBN-13: 8119534573

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It is not feasible to arrive at an accurate estimate of the total quantity of knowledge that has been accumulated as a direct consequence of man's activity. Every single day, millions of new tuples are added to the databases, and each of those tuples represents an observation, an experience that can be learned from it, and a situation that may occur again in the future in a way that is comparable to the one it happened in when it was first observed. As human beings, we have the innate capacity to gain knowledge from our experiences, and this is something that occurs constantly throughout our lives. Nevertheless, what does place when the number of occurrences to which we are exposed is more than our capacity to comprehend each of them? What would happen if a fact were to be repeated millions of times, but it would never happen precisely the same way again? What would the results be? What kind of outcomes may we anticipate? It is a subfield of artificial intelligence that focuses on learning from experience, or, to be more specific, the process of automatically extracting implicit knowledge from information that is stored in the form of data. This subfield was named after the concept of learning from experience. Machine learning, which is sometimes shortened as ML and referred to in certain contexts as ML, is sometimes referred to simply as ML. In this study, we will investigate two problems that have been solved in the actual world of business by using machine learning. These problems were faced by companies throughout the globe. Companies were tasked with overcoming both of these obstacles. The first of these responsibilities is to provide an accurate forecast of the final product quality that will be supplied by an oil and gas refinery, which is discussed in Section 2. The second component is a model that, as will be covered in Section 3, may be used in order to acquire an estimate of the amount of wear and tear that will be experienced by a collection of micro gas turbines. This will be accomplished by calculating the amount of wear and tear that can be expected from the collection of micro gas turbines. In the phrase that follows, we will talk about the theoretical components that are essential for the creation of our solutions. An explanation of the ML approaches that we have used may be found in Section 1.1 for any reader who is interested in reading it and would want to read it.

Mastering Machine Learning on AWS
Mastering Machine Learning on AWS

Author: Dr. Saket S.R. Mengle

Publisher: Packt Publishing Ltd

Published: 2019-05-20

Total Pages: 293

ISBN-13: 1789347505

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Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Practical Deep Learning for Cloud, Mobile, and Edge
Practical Deep Learning for Cloud, Mobile, and Edge

Author: Anirudh Koul

Publisher: O'Reilly Media

Published: 2019-10-14

Total Pages: 586

ISBN-13: 1492034835

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Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Practical Machine Learning on Databricks
Practical Machine Learning on Databricks

Author: Debu Sinha

Publisher: Packt Publishing Ltd

Published: 2023-11-24

Total Pages: 244

ISBN-13: 1801818290

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Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

Practical Machine Learning with H2O
Practical Machine Learning with H2O

Author: Darren Cook

Publisher: "O'Reilly Media, Inc."

Published: 2016-12-05

Total Pages: 300

ISBN-13: 149196457X

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"Learn how to construct machine learning and data analysis scalable for big data using H2O software, using sample data sets and several machine-learning techniques including deep learning, random forests, unsupervised learning and ensemble learning."--Provided by publisher.

Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision

Author: Valliappa Lakshmanan

Publisher: "O'Reilly Media, Inc."

Published: 2021-07-21

Total Pages: 482

ISBN-13: 1098102320

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This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

AWS certification guide - AWS Certified Machine Learning - Specialty
AWS certification guide - AWS Certified Machine Learning - Specialty

Author:

Publisher: Cybellium Ltd

Published:

Total Pages: 167

ISBN-13:

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AWS Certification Guide - AWS Certified Machine Learning – Specialty Unleash the Potential of AWS Machine Learning Embark on a comprehensive journey into the world of machine learning on AWS with this essential guide, tailored for those pursuing the AWS Certified Machine Learning – Specialty certification. This book is a valuable resource for professionals seeking to harness the power of AWS for machine learning applications. Inside, You'll Explore: Foundational to Advanced ML Concepts: Understand the breadth of AWS machine learning services and tools, from SageMaker to DeepLens, and learn how to apply them in various scenarios. Practical Machine Learning Scenarios: Delve into real-world examples and case studies, illustrating the practical applications of AWS machine learning technologies in different industries. Targeted Exam Preparation: Navigate the certification exam with confidence, thanks to detailed insights into the exam format, including specific chapters aligned with the certification objectives and comprehensive practice questions. Latest Trends and Best Practices: Stay at the forefront of machine learning advancements with up-to-date coverage of the latest AWS features and industry best practices. Written by a Machine Learning Expert Authored by an experienced practitioner in AWS machine learning, this guide combines in-depth knowledge with practical insights, providing a rich and comprehensive learning experience. Your Comprehensive Resource for ML Certification Whether you are deepening your existing machine learning skills or embarking on a new specialty in AWS, this book is your definitive companion, offering an in-depth exploration of AWS machine learning services and preparing you for the Specialty certification exam. Advance Your Machine Learning Career Beyond preparing for the exam, this guide is about mastering the complexities of AWS machine learning. It's a pathway to developing expertise that can be applied in innovative and transformative ways across various sectors. Start Your Specialized Journey in AWS Machine Learning Set off on your path to becoming an AWS Certified Machine Learning specialist. This guide is your first step towards mastering AWS machine learning and unlocking new opportunities in this exciting and rapidly evolving field. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Practical MLOps
Practical MLOps

Author: Noah Gift

Publisher: "O'Reilly Media, Inc."

Published: 2021-09-14

Total Pages: 461

ISBN-13: 1098102983

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Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware