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Author: Sonali Sahu
Publisher: Packt Publishing Ltd
ISBN: 1803233532
Category : Computers
Languages : en
Pages : 246
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Book Description
Build real-world artificial intelligence applications across industries with the help of intelligent document processing Key FeaturesTackle common document processing problems to extract value from any type of documentUnlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/MLApply your knowledge to solve real document analysis problems in various industry applicationsBook Description With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You'll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you'll have mastered the fundamentals of document processing with machine learning through practical implementation. What you will learnUnderstand the requirements and challenges in deriving insights from a documentExplore common stages in the intelligent document processing pipelineDiscover how AWS AI/ML can successfully automate IDP pipelinesFind out how to write clean and elegant Python code by leveraging AIGet to grips with the concepts and functionalities of AWS AI servicesExplore IDP across industries such as insurance, healthcare, finance, and the public sectorDetermine how to apply business rules in IDPBuild, train, and deploy models with serverless architecture for IDPWho this book is for This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.
Author: Sonali Sahu
Publisher: Packt Publishing Ltd
ISBN: 1803233532
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
Build real-world artificial intelligence applications across industries with the help of intelligent document processing Key FeaturesTackle common document processing problems to extract value from any type of documentUnlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/MLApply your knowledge to solve real document analysis problems in various industry applicationsBook Description With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You'll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you'll have mastered the fundamentals of document processing with machine learning through practical implementation. What you will learnUnderstand the requirements and challenges in deriving insights from a documentExplore common stages in the intelligent document processing pipelineDiscover how AWS AI/ML can successfully automate IDP pipelinesFind out how to write clean and elegant Python code by leveraging AIGet to grips with the concepts and functionalities of AWS AI servicesExplore IDP across industries such as insurance, healthcare, finance, and the public sectorDetermine how to apply business rules in IDPBuild, train, and deploy models with serverless architecture for IDPWho this book is for This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.
Author: Sonali Sahu
Publisher: Packt Publishing Ltd
ISBN: 1803233532
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
Build real-world artificial intelligence applications across industries with the help of intelligent document processing Key FeaturesTackle common document processing problems to extract value from any type of documentUnlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/MLApply your knowledge to solve real document analysis problems in various industry applicationsBook Description With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You'll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you'll have mastered the fundamentals of document processing with machine learning through practical implementation. What you will learnUnderstand the requirements and challenges in deriving insights from a documentExplore common stages in the intelligent document processing pipelineDiscover how AWS AI/ML can successfully automate IDP pipelinesFind out how to write clean and elegant Python code by leveraging AIGet to grips with the concepts and functionalities of AWS AI servicesExplore IDP across industries such as insurance, healthcare, finance, and the public sectorDetermine how to apply business rules in IDPBuild, train, and deploy models with serverless architecture for IDPWho this book is for This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.
Author: Manu Joseph
Publisher: Packt Publishing Ltd
ISBN: 1803232048
Category : Computers
Languages : en
Pages : 552
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Book Description
Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.
Author: Rajesh Kumar Dhanaraj
Publisher: John Wiley & Sons
ISBN: 1394185820
Category : Technology & Engineering
Languages : en
Pages : 357
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Book Description
This book is essential for anyone looking to understand how hyperautomation can revolutionize businesses by simplifying operations, reducing errors, and creating more intelligent and adaptable workplaces through the use of automation technologies such as artificial intelligence, machine learning, and robotic process automation. The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technology’s distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership. Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.
Author: Mona M
Publisher: Packt Publishing Ltd
ISBN: 1801815488
Category : Computers
Languages : en
Pages : 508
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Book Description
Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services Key FeaturesGet to grips with AWS AI services for NLP and find out how to use them to gain strategic insightsRun Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomesUnderstand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2IBook Description Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today's business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications. What you will learnAutomate various NLP workflows on AWS to accelerate business outcomesUse Amazon Textract for text, tables, and handwriting recognition from images and PDF filesGain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon ComprehendSet up end-to-end document processing pipelines to understand the role of humans in the loopDevelop NLP-based intelligent search solutions with just a few lines of codeCreate both real-time and batch document processing pipelines using PythonWho this book is for If you're an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful.
Author: David Ping
Publisher: Packt Publishing Ltd
ISBN: 1801070415
Category : Computers
Languages : en
Pages : 442
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Book Description
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
Author: Shreyas Subramanian
Publisher: John Wiley & Sons
ISBN: 1119821010
Category : Computers
Languages : en
Pages : 382
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Book Description
Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. You’ll also find: An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.
Author: Rabi Jay
Publisher: John Wiley & Sons
ISBN: 1394213069
Category : Computers
Languages : en
Pages : 763
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Book Description
Embrace emerging AI trends and integrate your operations with cutting-edge solutions Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You’ll also discover best practices on optimizing cloud infrastructure for scalability and automation. Enterprise AI in the Cloud helps you gain a solid understanding of: AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.
Author: Filippo Annunziata
Publisher: Edward Elgar Publishing
ISBN: 1035310724
Category : Law
Languages : en
Pages : 221
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Book Description
Artificial Intelligence and Market Abuse Legislation presents a wide-reaching interdisciplinary examination of the impact of AI on the EU Market Abuse Regulation (MAR).
Author: Subhashini Tripuraneni
Publisher: Packt Publishing Ltd
ISBN: 1789531470
Category : Computers
Languages : en
Pages : 411
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Book Description
Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key FeaturesExplore popular machine learning and deep learning services with their underlying algorithmsDiscover readily available artificial intelligence(AI) APIs on AWS like Vision and Language ServicesDesign robust architectures to enable experimentation, extensibility, and maintainability of AI appsBook Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learnGain useful insights into different machine and deep learning modelsBuild and deploy robust deep learning systems to productionTrain machine and deep learning models with diverse infrastructure specificationsScale AI apps without dealing with the complexity of managing the underlying infrastructureMonitor and Manage AI experiments efficientlyCreate AI apps using AWS pre-trained AI servicesWho this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
