Aws Ramp Up Guide Machine Learning

  aws ramp up guide machine learning: AWS Certified Machine Learning Study Guide Shreyas Subramanian, Stefan Natu, 2021-11-19 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.
  aws ramp up guide machine learning: Cloud Computing Demystified for Aspiring Professionals David Santana, Amit Malik, 2023-03-24 Gain in-depth knowledge of cloud computing concepts and apply them to accelerate your career in any cloud engineering role Key FeaturesGet to grips with key cloud computing concepts, cloud service providers, and best practicesExplore demonstrations for cloud computing models using real-world examplesAdopt the self-paced learning strategy and get industry-ready for cloud engineering rolesPurchase of the print or Kindle book includes a free eBook in the PDF formatBook Description If you want to upskill yourself in cloud computing domains to thrive in the IT industry, then you've come to the right place. Cloud Computing Demystified for Aspiring Professionals helps you to master cloud computing essentials and important technologies offered by cloud service providers needed to succeed in a cloud-centric job role. This book begins with an overview of transformation from traditional to modern-day cloud computing infrastructure, and various types and models of cloud computing. You'll learn how to implement secure virtual networks, virtual machines, and data warehouse resources including data lake services used in big data analytics — as well as when to use SQL and NoSQL databases and how to build microservices using multi-cloud Kubernetes services across AWS, Microsoft Azure, and Google Cloud. You'll also get step-by-step demonstrations of infrastructure, platform, and software cloud services and optimization recommendations derived from certified industry experts using hands-on tutorials, self-assessment questions, and real-world case studies. By the end of this book, you'll be ready to successfully implement cloud computing standardized concepts, services, and best practices in your workplace. What you will learnGain insights into cloud computing essentials and public, private, hybrid, and multi-cloud deployment modelsExplore core cloud computing services such as IaaS, PaaS, and SaaSDiscover major public cloud providers such as AWS, Microsoft, and GoogleUnlock the power of IaaS, PaaS, and SaaS with AWS, Azure, and GCPCreate secure networks, containers, Kubernetes, compute, databases, and API services on cloudDevelop industry-based cloud solutions using real-world examplesGet recommendations on exam preparation for cloud accreditationsWho this book is for The book is for aspiring cloud engineers, as well as college graduates, IT enthusiasts, and beginner-level cloud practitioners looking to get into cloud computing or transforming their career and upskilling themselves in a cloud engineering role in any industry. A basic understanding of networking, database development, and data analysis concepts and experience in programming languages such as Python and C# will help you get the most out of this book.
  aws ramp up guide machine learning: AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide Somanath Nanda, Weslley Moura, 2024-02-29 Prepare confidently for the AWS MLS-C01 certification with this comprehensive and up-to-date exam guide, accompanied by web-based tools such as mock exams, flashcards, and exam tips Key Features Gain proficiency in AWS machine learning services to excel in the MLS-C01 exam Build model training and inference pipelines and deploy machine learning models to the AWS cloud Practice on the go with the mobile-friendly bonus website, accessible with the book Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices—PCs, tablets, and smartphones. Throughout the book, you’ll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification.What you will learn Identify ML frameworks for specific tasks Apply CRISP-DM to build ML pipelines Combine AWS services to build AI/ML solutions Apply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformations Visualize relationships, comparisons, compositions, and distributions in the data Use data preparation techniques and AWS services for batch and real-time data processing Create training and inference ML pipelines with Sage Maker Deploy ML models in a production environment efficiently Who this book is for This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book.
  aws ramp up guide machine learning: The Holloway Guide to Technical Recruiting and Hiring Osman (Ozzie) Osman, 2022-01-10 Learn how the best teams hire software engineers and fill technical roles. The Holloway Guide to Technical Recruiting and Hiring is the authoritative guide to growing software engineering teams effectively, written by and for hiring managers, recruiters, interviewers, and candidates. Hiring is rated as one of the biggest obstacles to growth by most CEOs. Hiring managers, recruiters, and interviewers all wrestle with how to source candidates, interview fairly and effectively, and ultimately motivate the right candidates to accept offers. Yet the process is costly, frustrating, and often stressful or unfair to candidates. Anyone who cares about building effective software teams will return to this book again and again. Inside, you'll find know-how from some of the most insightful and experienced leaders and practitioners—senior engineers, recruiters, entrepreneurs, and hiring managers—who’ve built teams from early-stage startups to thousand-person engineering organizations. The lead author of this guide, Ozzie Osman, previously led product engineering at Quora and teams at Google, and built (and sold) his own startup. Additional contributors include Aditya Agarwal, former CTO of Dropbox; Jennifer Kim, former head of diversity at Lever; veteran recruiters and startup founders Jose Guardado (founder of Build Talent and former Y Combinator) and Aline Lerner (CEO of Interviewing.io); and over a dozen others. Recruiting and hiring can be done well, in a way that has a positive impact on companies, employees, and every candidate. With the right foundations and practice, teams and candidates can approach a stressful and difficult process with knowledge and confidence. Ask your employer if you can expense this book—it's one of the highest-leverage investments they can make in your team.
  aws ramp up guide machine learning: AWS certification guide - AWS Certified Machine Learning - Specialty , 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
  aws ramp up guide machine learning: AWS Certified AI & Machine Learning Specialist , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
  aws ramp up guide machine learning: The Definitive Guide to Machine Learning Operations in AWS Neel Sendas, Deepali Rajale, 2024-12-07 This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOPS tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.
  aws ramp up guide machine learning: Learn Amazon SageMaker Julien Simon, 2021-11-26 Swiftly build and deploy machine learning models without managing infrastructure and boost productivity using the latest Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature Store Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerOptimize the accuracy, cost, and fairness of your modelsCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Book Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnBecome well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and natural language processing (NLP) models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.
  aws ramp up guide machine learning: AWS Raoul Alongi, STRUGGLING TO COME TO GRIPS WITH CLOUD TECHNOLOGY? Amazon Web Services offer on-demand cloud services created with customer satisfaction in mind. AWS is one of the best cloud services out there, but starting the tech can be tricky if you don't have the right guide. This book was written exactly for anyone wanting to use cloud systems in their place of work, for the first time. It's one of the only books on the market that holds your hand and guides you step by step to setting up Amazon Web Services for yourself. You won't get this quality of cloud computing information anywhere else! It gets straight to the point allowing you get finished with setting up the ground-breaking technology FASTER. No hype! No fuss! Just pure value! This amazing book will cover: > Fundamentals of cloud computing; > Basic terminologies for the AWS cloud platform; > Cloud computing security (VERY IMPORTANT for businesses!); > Basics of SaaS, PaaS, IaaS cloud computing services; > Cloud computing development models; > Mobile Services with AWS Amplify, Pinpoint and AWS AppSync; > Network and Content Delivery; > Developer tools such as AWS Code Commit and AWS X-Ray; > Management tools such as Cloud Formation and AWS control tower; > Using managed Blockchain for Amazon Quantum Ledger Databases (QLDB); > Security, Identity and compliance; > Business applications such as Alexa, Amazon Chime and Toll (video conferencing); > Cost Management (Often Overlooked!); > Desktop and App streaming; > Which one is better? Artificial intelligence or Machine Learning?; > Game Dev (Fantastic for Indie Developers!); > Internet of things (IOT); > And so much more! Finally, you will be able to effectively utilise the resource that is AWS cloud services, in record time and you'll have fun along the way!
  aws ramp up guide machine learning: Amazon SageMaker Best Practices Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode, 2021-09-24 Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key FeaturesLearn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in productionAutomate end-to-end machine learning workflows with Amazon SageMaker and related AWSDesign, architect, and operate machine learning workloads in the AWS CloudBook Description Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows. What you will learnPerform data bias detection with AWS Data Wrangler and SageMaker ClarifySpeed up data processing with SageMaker Feature StoreOvercome labeling bias with SageMaker Ground TruthImprove training time with the monitoring and profiling capabilities of SageMaker DebuggerAddress the challenge of model deployment automation with CI/CD using the SageMaker model registryExplore SageMaker Neo for model optimizationImplement data and model quality monitoring with Amazon Model MonitorImprove training time and reduce costs with SageMaker data and model parallelismWho this book is for This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.
  aws ramp up guide machine learning: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
  aws ramp up guide machine learning: Mastering Machine Learning on AWS Dr. Saket S.R. Mengle, Maximo Gurmendez, 2019-05-20 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.
  aws ramp up guide machine learning: Cloud Native AI and Machine Learning on AWS Premkumar Rangarajan, David Bounds, 2023-02-14 Bring elasticity and innovation to Machine Learning and AI operations KEY FEATURES ● Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML. ● Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS. ● Includes ready-made, purpose-built models as AI services and proven methods to adopt MLOps techniques. DESCRIPTION Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation. In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services. WHAT YOU WILL LEARN ● Learn how to build, deploy, and manage large-scale AI and ML applications on AWS. ● Get your hands dirty with AWS AI services like SageMaker, Comprehend, Rekognition, Lookout, and AutoML. ● Master data transformation, feature engineering, and model training with Amazon SageMaker modules. ● Use neural networks, distributed learning, and deep learning algorithms to improve ML models. ● Use AutoML, SageMaker Canvas, and Autopilot for Model Deployment and Evaluation. ● Acquire expertise with Amazon SageMaker Studio, Jupyter Server, and ML frameworks such as TensorFlow and MXNet. WHO THIS BOOK IS FOR Data Engineers, Data Scientists, AWS and Cloud Professionals who are comfortable with machine learning and the fundamentals of Python will find this book powerful. Familiarity with AWS would be helpful but is not required. TABLE OF CONTENTS 1. Introducing the ML Workflow 2. Hydrating the Data Lake 3. Predicting the Future With Features 4. Orchestrating the Data Continuum 5. Casting a Deeper Net (Algorithms and Neural Networks) 6. Iteration Makes Intelligence (Model Training and Tuning) 7. Let George Take Over (AutoML in Action) 8. Blue or Green (Model Deployment Strategies) 9. Wisdom at Scale with Elastic Inference 10. Adding Intelligence with Sensory Cognition 11. AI for Industrial Automation 12. Operationalized Model Assembly (MLOps and Best Practices)
  aws ramp up guide machine learning: Learning AWS Aurobindo Sarkar, Amit Shah, 2018-02-01 Discover techniques and tools for building serverless applications with AWS Key Features Get well-versed with building and deploying serverless APIs with microservices Learn to build distributed applications and microservices with AWS Step Functions A step-by-step guide that will get you up and running with building and managing applications on the AWS platform Book Description Amazon Web Services (AWS) is the most popular and widely-used cloud platform. Administering and deploying application on AWS makes the applications resilient and robust. The main focus of the book is to cover the basic concepts of cloud-based development followed by running solutions in AWS Cloud, which will help the solutions run at scale. This book not only guides you through the trade-offs and ideas behind efficient cloud applications, but is a comprehensive guide to getting the most out of AWS. In the first section, you will begin by looking at the key concepts of AWS, setting up your AWS account, and operating it. This guide also covers cloud service models, which will help you build highly scalable and secure applications on the AWS platform. We will then dive deep into concepts of cloud computing with S3 storage, RDS and EC2. Next, this book will walk you through VPC, building realtime serverless environments, and deploying serverless APIs with microservices. Finally, this book will teach you to monitor your applications, and automate your infrastructure and deploy with CloudFormation. By the end of this book, you will be well-versed with the various services that AWS provides and will be able to leverage AWS infrastructure to accelerate the development process. What you will learn Set up your AWS account and get started with the basic concepts of AWS Learn about AWS terminology and identity access management Acquaint yourself with important elements of the cloud with features such as computing, ELB, and VPC Back up your database and ensure high availability by having an understanding of database-related services in the AWS cloud Integrate AWS services with your application to meet and exceed non-functional requirements Create and automate infrastructure to design cost-effective, highly available applications Who this book is for If you are an I.T. professional or a system architect who wants to improve infrastructure using AWS, then this book is for you. It is also for programmers who are new to AWS and want to build highly efficient, scalable applications.
  aws ramp up guide machine learning: Machine Learning in the AWS Cloud Abhishek Mishra, 2019-08-13 Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
  aws ramp up guide machine learning: AWS Certified Cloud Practitioner Exam Study Guide Richie Miller, If you want to become an AWS Certified Cloud Practitioner, this book is for you! 3 BOOKS IN 1 DEAL! · BOOK 1 - AWS CLOUD PRACTITIONER: CLOUD COMPUTING ESSENTIALS · BOOK 2 - AWS CLOUD COMPUTING: INTRODUCTION TO CORE SERVICES · BOOK 3 - AWS CLOUD SECURITY: BEST PRACTICES FOR SMALL AND MEDIUM BUSINESSES This book will help you prepare for the AWS Certified Cloud Practitioner exam (Exam Code CLF-C01). In fact, this book is designed to be a one‑stop shop for you. It has everything that you need to get ready for the exam. This book will not just help you through sample questions, but it will also walk you through and teach you how to think about the exam questions to become an AWS Certified Cloud Practitioner. GET THIS BOOK NOW TO DISCOVER AWS CLOUD TODAY!
  aws ramp up guide machine learning: Learning Amazon Web Services (AWS) Mark Wilkins, 2019-07-02 The Practical, Foundational Technical Introduction to the World’s #1 Cloud Platform Includes access to several hours of online training video: Mark Wilkins’ expert training video library guides you through setting up core services and prepares you to deploy your own apps and resources. Learning Amazon Web Services (AWS) is the perfect foundational resource for all administrators, developers, project managers, and other IT professionals who want to plan and deploy AWS services and/or earn AWS certification. Top cloud trainer and evangelist Mark Wilkins teaches best practices that align with Amazon’s Well-Architected Framework, introduces key concepts in the context of a running case study, carefully explains how core AWS services operate and integrate, and offers extensively tested tips for maximizing flexibility, security, and value. Companion online videos guide you step-by-step through setting AWS compute, storage, networking, scale, security, automation, and more. Balance cost, compliance, and latency in your service designs Choose the right networking options for your virtual private cloud (VPC) Build, host, launch, manage, and budget for EC2 compute services Plan for scale and resiliency, and make informed decisions about AWS storage Enforce strict security, and automate to improve operational efficiency This book with companion training videos is a valuable learning tool for anyone seeking to demonstrate expertise through formal certification. WEB EDITION: All buyers of the book or ebook can register your book for access to a free online Web Edition of this title, which included videos embedded within the text, plus updates as they become available.
  aws ramp up guide machine learning: Practical Machine Learning with AWS Himanshu Singh, 2021 Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. You will: Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS.
  aws ramp up guide machine learning: AWS Certified Cloud Practitioner Study Guide with Online Labs Ben Piper, David Clinton, 2020-07-28 Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Cloud Practitioner Study Guide to give you the same experience working in these labs as you prepare for the Certified Cloud Practitioner Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Cloud Practitioner. The AWS Certified Cloud Practitioner Study Guide: Exam CLF-C01 provides a solid introduction to this industry-leading technology, relied upon by thousands of businesses across the globe, as well as the resources you need to prove your knowledge in the AWS Certification Exam. This guide offers complete and thorough treatment of all topics included in the exam, beginning with a discussion of what the AWS cloud is and its basic global infrastructure and architectural principles. Other chapters dive into the technical, exploring core characteristics of deploying and operating in the AWS Cloud Platform, as well as basic security and compliance aspects and the shared security model. In addition, the text identifies sources of documentation or technical assistance, such as white papers or support tickets. To complete their coverage, the authors discuss the AWS Cloud value proposition and define billing, account management, and pricing models. This includes describing the key services AWS can provide and their common use cases (e.g., compute, analytics, etc.). Distinguish yourself as an expert by obtaining a highly desirable certification in a widely used platform Hone your skills and gain new insights on AWS whether you work in a technical, managerial, sales, purchasing, or financial field Fully prepare for this new exam using expert content and real-world knowledge, key exam essentials, chapter review questions, and other textual resources Benefit from access to the Sybex online interactive learning environment and test bank, including chapter tests, practice exams, key term glossary, and electronic flashcards XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months unlimited access to XtremeLabs AWS Certified Cloud Practitioner Labs with 8 unique lab modules based on the book. The AWS Certified Cloud Practitioner Study Guide is essential reading for any professional in IT or other fields that work directly with AWS, soon-to-be graduates studying in those areas, or anyone hoping to prove themselves as an AWS Certified Cloud Practitioner.
  aws ramp up guide machine learning: Machine Learning with AWS Jeffrey Jackovich, Ruze Richards, 2018-11-02 Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.
  aws ramp up guide machine learning: Natural Language Processing with AWS AI Services Mona M, Premkumar Rangarajan, Julien Simon, 2021-11-26 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.
  aws ramp up guide machine learning: Hands-On Artificial Intelligence on Amazon Web Services Subhashini Tripuraneni, Charles Song, 2019-10-04 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.
  aws ramp up guide machine learning: Applied Machine Learning and High-Performance Computing on AWS Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter, 2022-12-30 Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
  aws ramp up guide machine learning: Aws Jim Cloud, 2020-10-03 Discover the infinite world of Amazon Web Services (AWS) and become a master of cloud computing! Amazon Web Services is the world's most comprehensive and broadly adopted cloud platform. That is why millions of people, developing startups, corporations, and even government agencies use 175 features on an everyday basis. If AWS sounds like a must-have tool, you are right, but how about learning how to use this universal platform? Don't worry, we have figured it out for you with AWS: The Ultimate Step-by-Step Guide from Beginners to Advanced for Amazon Web Services. This comprehensive volume can teach you everything from the basis of AWS to more advanced functions, so you can ace this cloud platform and get the security and innovation you deserve. Written by top experts in cloud computing and AWS, this easy-to-follow guide is great for getting a grasp at what Amazon Web Services can do for you, as well as how to master this new and improved cloud platform. What can you learn with this AWS handbook? You will start your journey by understanding what AWS and what it can really do for you and your business. Afterward, you will get a short and comprehensive background of how this cloud platform was born and its history. Learning the first steps of AWS is going to be simple once you have a view of what cloud computing is and what are the main benefits of AWS. Now that you have acquired the basic knowledge, you can move on to the more advanced steps: learning about service development for website owners, AWS storage devices, big data and notification services, security, database, and cost management. With this book, you can: Improve your knowledge about cloud systems and data analysis Get a handle on the basic concepts of AWS Make it easier to work with the 175 features of the AWS platform Learn about the myths surrounding AWS that you should stop and get the real facts instead Understand why knowing about cloud computing is a real asset for you and your company Learn how AWS was born and developed by the experts behind it Discover some of the weaknesses of the system and how to make them work in your advantage Understand why the feature of cloud computing for the big players lies in Amazon Elastic Compute Clouding Learn the role of AWS in advanced systems and innovative ideas such as big data, machine learning, and AI Become a better professional that can help you advance in your career or grow your business Enjoy a secured database system that will never fail you or your company Learn how to use platform available in almost any region of the world, with no compromises on location Experience innovative ideas and become a highly skilled professional! And much more! Still having doubts? Don't worry, this book is easy to follow and will thoroughly explain every concept in a simple way that is easy to understand by everyone. Don't hesitate and start your AWS learning journey NOW!  Click Buy Now With 1-Click or Buy Now to get started
  aws ramp up guide machine learning: AWS for Solutions Architects Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed, Dr. Siddhartha Choubey Ph.D, 2023-04-28 Become a master Solutions Architect with this comprehensive guide, featuring cloud design patterns and real-world solutions for building scalable, secure, and highly available systems Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Gain expertise in automating, networking, migrating, and adopting cloud technologies using AWS Use streaming analytics, big data, AI/ML, IoT, quantum computing, and blockchain to transform your business Upskill yourself as an AWS solutions architect and explore details of the new AWS certification Book Description Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Packed with all-new content, this book is a must-have guide for anyone looking to build scalable cloud solutions and drive digital transformation using AWS. This updated edition offers in-depth guidance for building cloud solutions using AWS. It provides detailed information on AWS well-architected design pillars and cloud-native design patterns. You'll learn about networking in AWS, big data and streaming data processing, CloudOps, and emerging technologies such as machine learning, IoT, and blockchain. Additionally, the book includes new sections on storage in AWS, containers with ECS and EKS, and data lake patterns, providing you with valuable insights into designing industry-standard AWS architectures that meet your organization's technological and business requirements. Whether you're an experienced solutions architect or just getting started with AWS, this book has everything you need to confidently build cloud-native workloads and enterprise solutions. What you will learn Optimize your Cloud Workload using the AWS Well-Architected Framework Learn methods to migrate your workload using the AWS Cloud Adoption Framework Apply cloud automation at various layers of application workload to increase efficiency Build a landing zone in AWS and hybrid cloud setups with deep networking techniques Select reference architectures for business scenarios, like data lakes, containers, and serverless apps Apply emerging technologies in your architecture, including AI/ML, IoT and blockchain Who this book is for This book is for application and enterprise architects, developers, and operations engineers who want to become well versed with AWS architectural patterns, best practices, and advanced techniques to build scalable, secure, highly available, highly tolerant, and cost-effective solutions in the cloud. Existing AWS users are bound to learn the most, but it will also help those curious about how leveraging AWS can benefit their organization. Prior knowledge of any computing language is not needed, and there's little to no code. Prior experience in software architecture design will prove helpful.
  aws ramp up guide machine learning: Learning AWS IoT Agus Kurniawan, 2018-01-29 Learn to use AWS IoT services to build your connected applications with the help of this comprehensive guide. Key Features Gets you started with AWS IoT and its functionalities Learn different modules of AWS IoT with practical use cases. Learn to secure your IoT communication Book Description The Internet of Things market increased a lot in the past few years and IoT development and its adoption have showed an upward trend. Analysis and predictions say that Enterprise IoT platforms are the future of IoT. AWS IoT is currently leading the market with its wide range of device support SDKs and versatile management console. This book initially introduces you to the IoT platforms, and how it makes our IoT development easy. It then covers the complete AWS IoT Suite and how it can be used to develop secure communication between internet-connected things such as sensors, actuators, embedded devices, smart applications, and so on. The book also covers the various modules of AWS: AWS Greengrass, AWS device SDKs, AWS IoT Platform, AWS Button, AWS Management consoles, AWS-related CLI, and API references, all with practical use cases. Near the end, the book supplies security-related best practices to make bi-directional communication more secure. When you've finished this book, you'll be up-and-running with the AWS IoT Suite, and building IoT projects. What you will learn Implement AWS IoT on IoT projects Learn the technical capabilities of AWS IoT and IoT devices Create IoT-based AWS IoT projects Choose IoT devices and AWS IoT platforms to use based on the kind of project you need to build Deploy AWS Greengrass and AWS Lambda Develop program for AWS IoT Button Visualize IoT AWS data Build predictive analytics using AWS IoT and AWS Machine Learning Who this book is for This book is for anyone who wants to get started with the AWS IoT Suite and implement it with practical use cases. This book acts as an extensive guide, on completion of which you will be in a position to start building IoT projects using AWS IoT platform and using cloud services for your projects.
  aws ramp up guide machine learning: Aws Maverick Koston, 2020-01-21 Upgrade Your Business and Become an Expert on Amazon Web Services! Do you want your company to be as profitable as Netflix, Spotify, and Ubisoft? You've heard about Amazon Web Services (AWS) but don't know how to implement it in your business? If you haven't heard about AWS, it's time to get familiar with it! AWS took the IT world by storm and is a leading trend in the cloud-computing sector. It's not a coincidence that a highly successful Netflix has moved all of its IT operations on AWS. Simply stated, AWS is the best option for any business - you get better performance and security for less money! This comprehensive guide on AWS will show you how to transform your company by using the AWS tools and strategies. Unlike many similar instruction manuals and guides, you won't find this book to be too technical and hard to understand. Here's just a fragment of what you get with this book: A complete guide on cloud computing and its advantages Instruction on the types and the cloud computing deployment A comprehensive introduction to AWS and possible uses A guide on the AWS Framework and design Instructions on how to start with AWS How to utilize AWS for machine learning and AI projects A guide on AWS Certifications to upgrade your skills further Additional facts, tips and tricks on Amazon and AWS And much more! The best part of AWS is that you don't have to have an IT industry degree, to implement it in your business. All you need is a great coach, and you'll find an excellent one in this book! No matter if your business is small or big, AWS will make it highly profitable, more secure and recognizable! So, what are you waiting for ?? Scroll up, click on Buy Now with 1-Click, and Get Your Copy Now!
  aws ramp up guide machine learning: Time Series Analysis on AWS Michaël Hoarau, 2022-02-28 Leverage AWS AI/ML managed services to generate value from your time series data Key FeaturesSolve modern time series analysis problems such as forecasting and anomaly detectionGain a solid understanding of AWS AI/ML managed services and apply them to your business problemsExplore different algorithms to build applications that leverage time series dataBook Description Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes. The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data. By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis. What you will learnUnderstand how time series data differs from other types of dataExplore the key challenges that can be solved using time series dataForecast future values of business metrics using Amazon ForecastDetect anomalies and deliver forewarnings using Lookout for EquipmentDetect anomalies in business metrics using Amazon Lookout for MetricsVisualize your predictions to reduce the time to extract insightsWho this book is for If you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.
  aws ramp up guide machine learning: AWS Certified Cloud Solutions Architect Cybellium, 2024-09-01 Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
  aws ramp up guide machine learning: AWS Certified Machine Learning Study Guide: Associ Ate (MLA-C01) Exam Cabianca, 2025-10-21
  aws ramp up guide machine learning: Intelligent Document Processing with AWS AI/ML Sonali Sahu, 2022-10-21 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.
  aws ramp up guide machine learning: Machine Learning Engineering on AWS Joshua Arvin Lat, 2022-10-27 Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key FeaturesGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learnFind out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
  aws ramp up guide machine learning: Building Scalable Deep Learning Pipelines on AWS Abdelaziz Testas, 2024-12-03 This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies--such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3--to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will Learn Maximize AWS services for scalable and high-performance deep learning architectures Harness the capacity of PyTorch and TensorFlow for advanced neural network development Utilize PySpark for efficient distributed data processing on AWS Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deployment Who This Book Is For Data scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantage
  aws ramp up guide machine learning: AI as a Service Peter Elger, Eóin Shanaghy, 2020-09-05 AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Summary Companies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development—and developers who can design, develop, implement, and maintain these systems will be in high demand! AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Cloud-based AI services can automate a variety of labor intensive business tasks in areas such as customer service, data analysis, and financial reporting. The secret is taking advantage of pre-built tools like Amazon Rekognition for image analysis or AWS Comprehend for natural language processing. That way, there’s no need to build expensive custom software. Artificial Intelligence (AI), a machine’s ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more. With the exciting combination of serverless computing and AI, software developers now have enormous power to improve their businesses’ existing systems and rapidly deploy new AI-enabled platforms. And to get on this fast-moving train, you don’t have to invest loads of time and effort in becoming a data scientist or AI expert, thanks to cloud platforms and the readily available off-the-shelf cloud-based AI services! About the book AI as a Service is a fast-paced guide to harnessing the power of cloud-based solutions. You’ll learn to build real-world apps—such as chatbots and text-to-speech services—by stitching together cloud components. Work your way from small projects to large data-intensive applications. What's inside - Apply cloud AI services to existing platforms - Design and build scalable data pipelines - Debug and troubleshoot AI services - Start fast with serverless templates About the reader For software developers familiar with cloud basics. About the author Peter Elger and Eóin Shanaghy are founders and CEO/CTO of fourTheorem, a software solutions company providing expertise on architecture, DevOps, and machine learning. Table of Contents PART 1 - FIRST STEPS 1 A tale of two technologies 2 Building a serverless image recognition system, part 1 3 Building a serverless image recognition system, part 2 PART 2 - TOOLS OF THE TRADE 4 Building and securing a web application the serverless way 5 Adding AI interfaces to a web application 6 How to be effective with AI as a Service 7 Applying AI to existing platforms PART 3 - BRINGING IT ALL TOGETHER 8 Gathering data at scale for real-world AI 9 Extracting value from large data sets with AI
  aws ramp up guide machine learning: Aws A. W. S. Smith, 2019-10-18 NEW DESCRIPTION BY PAT5RR Long version: Pelaton uses it to power its live leaderboard. Cerner needs it to power machine learning and artificial intelligence. Expedia is using it for more and more of its critical apps. From GE Healthcare to Kellogg's, from Dow Jones to Adobe, companies rely on it to give them speed, flexibility--including to scale up and down quickly--and solutions to specific problems. It is Amazon Web Services (AWS), which combines cloud hosting with a suite of problem-solving services. Learning how to use AWS and understanding its pros and cons can add a critical skill to your résumé or help you make informed decisions for your company. AWS hosting is offered for both Windows and Linux operating systems and works with various platforms, including WordPress, Ruby, Drupal, Magento, Joomla, and Dot Net. Scheduled backups, dispersed data center locations, elastic load balancing, and content delivery are among the services offered by AWS. Amazon's pricing structure is different and more complex than what other cloud providers offer, so the average webmaster may struggle to understand what is needed and what it will cost. Amazon Web Services is a step-by-step guide to becoming an expert at Amazon Web Services. While comprehensive, it is suitable for beginners and easy to understand, moving from the basics to the specifics with these topics: Basics of cloud computing Importance of cloud computing in business Getting started with Amazon Web Services Pros and cons, startup costs Your business and web services Managing the platform, services, and apps Terminology Search engine optimization AWS for managers AWS for Amazon users Getting published with Amazon Publishing Making money with Amazon store services EC2 & S3 Amazon Prime Air, the drone delivery service now in development Kindle Fire Tablet Tutorials Amazon's secret plan and much more! Amazon dominates markets and creates new products and services. If you don't understand Amazon Web Services, there is no better time to start learning. Don't let excuses such as a busy schedule keep you from finding a crucial partner along the path to your goals. Buy [title of book] to get started now.
  aws ramp up guide machine learning: AWS Certified Data Analytics Study Guide Asif Abbasi, 2020-12-01 Move your career forward with AWS certification! Prepare for the AWS Certified Data Analytics Specialty Exam with this thorough study guide This comprehensive study guide will help assess your technical skills and prepare for the updated AWS Certified Data Analytics exam. Earning this AWS certification will confirm your expertise in designing and implementing AWS services to derive value from data. The AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is designed for business analysts and IT professionals who perform complex Big Data analyses. This AWS Specialty Exam guide gets you ready for certification testing with expert content, real-world knowledge, key exam concepts, and topic reviews. Gain confidence by studying the subject areas and working through the practice questions. Big data concepts covered in the guide include: Collection Storage Processing Analysis Visualization Data security AWS certifications allow professionals to demonstrate skills related to leading Amazon Web Services technology. The AWS Certified Data Analytics Specialty (DAS-C01) Exam specifically evaluates your ability to design and maintain Big Data, leverage tools to automate data analysis, and implement AWS Big Data services according to architectural best practices. An exam study guide can help you feel more prepared about taking an AWS certification test and advancing your professional career. In addition to the guide’s content, you’ll have access to an online learning environment and test bank that offers practice exams, a glossary, and electronic flashcards.
  aws ramp up guide machine learning: Effective Amazon Machine Learning Alexis Perrier, 2017-04-25 Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity Learn the What's next? of machine learning—machine learning on the cloud—with this unique guide Create web services that allow you to perform affordable and fast machine learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required. What You Will Learn Learn how to use the Amazon Machine Learning service from scratch for predictive analytics Gain hands-on experience of key Data Science concepts Solve classic regression and classification problems Run projects programmatically via the command line and the Python SDK Leverage the Amazon Web Service ecosystem to access extended data sources Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. Style and approach This book will include use cases you can relate to. In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease.
  aws ramp up guide machine learning: AWS Certified Cloud Developer – Associate Cybellium, 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
  aws ramp up guide machine learning: Data Science mit AWS Chris Fregly, Antje Barth, 2022-04-13 Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können.
  aws ramp up guide machine learning: Machine Learning for Beginners Samuel Hack, 2019-10-10 Are you interested in learning about the amazing capabilities of machine learning, but you're worried it will be just too complicated? Or are you a programmer looking for a solid introduction into this field? Then keep reading Machine learning is an incredible technology which we're only just beginning to understand. Those who break into this industry early will reap the rewards as this field grows more and more important to businesses the world over. And the good news is, it's not too late to start! This guide breaks down the fundamentals of machine learning in a way that anyone can understand. With reference to the different kinds of machine learning models, neural networks, and the way these models learn data, you'll find everything you need to know to get started with machine learning in a concise, easy-to-understand way. Here's what you'll discover inside: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Supervised and Unsupervised Learning The Power of Neural Networks Reinforcement Learning and Ensemble Modeling Random Forests and Decision Trees Must-Have Programming Tools And Much More! Whether you're already a programmer or if you're a complete beginner, now you can break into machine learning in no time! Covering all the basics from simple decision trees to the complex decision-making processes which mirror our own brains, Machine Learning for Beginners is your comprehensive introduction to this amazing field! Buy now to discover how you can get started with machine learning today! Scroll Up and Click the Buy Now Button to Get Your Copy!
AWS Management Console
Manage your AWS cloud resources easily through a web-based interface using the AWS Management Console.

Cloud Computing Services - Amazon Web Services (AWS)
Amazon Q is the generative AI-powered assistant from AWS that helps you streamline processes, enhance decision making, and boost productivity. Amazon Q has many new capabilities: Build …

What is AWS? - Cloud Computing with AWS - Amazon Web Services
For over 17 years, AWS has been delivering cloud services to millions of customers around the world running a wide variety of use cases. AWS has the most operational experience, at …

Free Cloud Computing Services - AWS Free Tier
Gain hands-on experience with the AWS platform, products, and services for free with the AWS Free Tier offerings. Browse 100 offerings for AWS free tier services.

Getting Started - Cloud Computing Tutorials for Building on AWS
Learn the fundamentals and start building on AWS now · Get to Know the AWS Cloud · Launch Your First Application · Visit the technical resource centers.

Welcome to AWS Documentation
Welcome to AWS Documentation

Sign in to the AWS Management Console - AWS Sign-In
Learn how to sign in to your AWS account and what credentials are required. Includes tutorials on how to sign in to the AWS Management Console as a root user and IAM users, and how to …

AWS Training and Certification
Begin learning by accessing 600+ free digital courses, curated by the experts at AWS. Unlock diverse lab experiences and more by becoming an AWS Skill Builder subscriber.

How to Create an AWS Account
Creating an account is the starting point to provide access to AWS services and resources. Follow these steps to set up your account.

Getting Started with AWS Cloud Essentials
Gain familiarity with core concepts of cloud computing and the AWS Cloud. Get the answers to common questions about cloud computing and explore best practices for building on AWS.

AWS Management Console
Manage your AWS cloud resources easily through a web-based interface using the AWS Management Console.

Cloud Computing Services - Amazon Web Services (AWS)
Amazon Q is the generative AI-powered assistant from AWS that helps you streamline processes, enhance decision making, and boost productivity. Amazon Q has many new capabilities: Build …

What is AWS? - Cloud Computing with AWS - Amazon Web …
For over 17 years, AWS has been delivering cloud services to millions of customers around the world running a wide variety of use cases. AWS has the most operational experience, at …

Free Cloud Computing Services - AWS Free Tier
Gain hands-on experience with the AWS platform, products, and services for free with the AWS Free Tier offerings. Browse 100 offerings for AWS free tier services.

Getting Started - Cloud Computing Tutorials for Building on AWS
Learn the fundamentals and start building on AWS now · Get to Know the AWS Cloud · Launch Your First Application · Visit the technical resource centers.

Welcome to AWS Documentation
Welcome to AWS Documentation

Sign in to the AWS Management Console - AWS Sign-In
Learn how to sign in to your AWS account and what credentials are required. Includes tutorials on how to sign in to the AWS Management Console as a root user and IAM users, and how to …

AWS Training and Certification
Begin learning by accessing 600+ free digital courses, curated by the experts at AWS. Unlock diverse lab experiences and more by becoming an AWS Skill Builder subscriber.

How to Create an AWS Account
Creating an account is the starting point to provide access to AWS services and resources. Follow these steps to set up your account.

Getting Started with AWS Cloud Essentials
Gain familiarity with core concepts of cloud computing and the AWS Cloud. Get the answers to common questions about cloud computing and explore best practices for building on AWS.