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Mastering Machine Learning on AWS

Mastering Machine Learning on AWS
Author: Dr. Saket S.R. Mengle
Publisher: Packt Publishing Ltd
Total Pages: 293
Release: 2019-05-20
Genre: Computers
ISBN: 1789347505
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Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Mastering Machine Learning on AWS

Mastering Machine Learning on AWS
Author: Dr. Saket S.R. Mengle
Publisher: Packt Publishing Ltd
Total Pages: 293
Release: 2019-05-20
Genre: Computers
ISBN: 1789347505
GET BOOK

Download Mastering Machine Learning on AWS Book in PDF, ePub and Kindle

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 certification guide - AWS Certified Machine Learning - Specialty

AWS certification guide - AWS Certified Machine Learning - Specialty
Author:
Publisher: Cybellium Ltd
Total Pages: 167
Release:
Genre: Computers
ISBN:
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AWS Certification Guide - AWS Certified Machine Learning – Specialty Unleash the Potential of AWS Machine Learning Embark on a comprehensive journey into the world of machine learning on AWS with this essential guide, tailored for those pursuing the AWS Certified Machine Learning – Specialty certification. This book is a valuable resource for professionals seeking to harness the power of AWS for machine learning applications. Inside, You'll Explore: Foundational to Advanced ML Concepts: Understand the breadth of AWS machine learning services and tools, from SageMaker to DeepLens, and learn how to apply them in various scenarios. Practical Machine Learning Scenarios: Delve into real-world examples and case studies, illustrating the practical applications of AWS machine learning technologies in different industries. Targeted Exam Preparation: Navigate the certification exam with confidence, thanks to detailed insights into the exam format, including specific chapters aligned with the certification objectives and comprehensive practice questions. Latest Trends and Best Practices: Stay at the forefront of machine learning advancements with up-to-date coverage of the latest AWS features and industry best practices. Written by a Machine Learning Expert Authored by an experienced practitioner in AWS machine learning, this guide combines in-depth knowledge with practical insights, providing a rich and comprehensive learning experience. Your Comprehensive Resource for ML Certification Whether you are deepening your existing machine learning skills or embarking on a new specialty in AWS, this book is your definitive companion, offering an in-depth exploration of AWS machine learning services and preparing you for the Specialty certification exam. Advance Your Machine Learning Career Beyond preparing for the exam, this guide is about mastering the complexities of AWS machine learning. It's a pathway to developing expertise that can be applied in innovative and transformative ways across various sectors. Start Your Specialized Journey in AWS Machine Learning Set off on your path to becoming an AWS Certified Machine Learning specialist. This guide is your first step towards mastering AWS machine learning and unlocking new opportunities in this exciting and rapidly evolving field. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Cloud Native AI and Machine Learning on AWS

Cloud Native AI and Machine Learning on AWS
Author: Premkumar Rangarajan
Publisher: BPB Publications
Total Pages: 366
Release: 2023-02-14
Genre: Computers
ISBN: 9355513267
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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)

Mastering AWS Certified Machine Learning - Specialty

Mastering AWS Certified Machine Learning - Specialty
Author: Innoware Pjp
Publisher: Independently Published
Total Pages: 0
Release: 2023-07-22
Genre:
ISBN:
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Mastering AWS Certified Machine Learning - Specialty: Exam Preparation Guide Table of Contents Chapter 1: Introduction to AWS Certified Machine Learning - Specialty Exam Chapter 2: AWS Machine Learning Fundamentals Chapter 3: Data Engineering on AWS Chapter 4: Exploratory Data Analysis and Feature Engineering Chapter 5: Model Selection and Evaluation Chapter 6: Deploying Machine Learning Models on AWS Chapter 7: AWS Machine Learning Services in Action Chapter 8: Best Practices for Deploying and Managing Machine Learning Applications on AWS Chapter 9: Final Exam Preparation Chapter 10: Real-World Machine Learning Applications on AWS Chapter 11: Emerging Trends in Machine Learning on AWS Chapter 12: Machine Learning Ethics and Responsible AI on AWS Chapter 13: Case Studies of Machine Learning on AWS Chapter 14: AWS Machine Learning Services Deep Dive Chapter 15: Advanced Machine Learning Techniques Chapter 16: Deploying Machine Learning Models on AWS Chapter 17: Model Monitoring and Management on AWS Chapter 18: Model Governance and Ethical Considerations Chapter 19: Model Explainability and Interpretability Chapter 20: Deploying Machine Learning Models at Scale on AWS

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide
Author: Somanath Nanda
Publisher: Packt Publishing Ltd
Total Pages: 338
Release: 2021-03-19
Genre: Computers
ISBN: 1800568436
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Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence Key Features Get to grips with core machine learning algorithms along with AWS implementation Build model training and inference pipelines and deploy machine learning models to the Amazon Web Services (AWS) cloud Learn all about the AWS services available for machine learning in order to pass the MLS-C01 exam Book DescriptionThe AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.What you will learn Understand all four domains covered in the exam, along with types of questions, exam duration, and scoring Become well-versed with machine learning terminologies, methodologies, frameworks, and the different AWS services for machine learning Get to grips with data preparation and using AWS services for batch and real-time data processing Explore the built-in machine learning algorithms in AWS and build and deploy your own models Evaluate machine learning models and tune hyperparameters Deploy machine learning models with the AWS infrastructure Who this book is for This AWS book is for professionals and students who want to prepare for and pass the AWS Certified Machine Learning Specialty exam or gain deeper knowledge of machine learning with a special focus on AWS. Beginner-level knowledge of machine learning and AWS services is necessary before getting started with this book.

Data Science on AWS

Data Science on AWS
Author: Chris Fregly
Publisher: "O'Reilly Media, Inc."
Total Pages: 524
Release: 2021-04-07
Genre: Computers
ISBN: 1492079367
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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

Generative AI on AWS

Generative AI on AWS
Author: Chris Fregly
Publisher: "O'Reilly Media, Inc."
Total Pages: 323
Release: 2023-11-13
Genre:
ISBN: 1098159187
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Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock

Ace the AWS Certified Machine Learning Specialty (MLS-C01) Certification

Ace the AWS Certified Machine Learning Specialty (MLS-C01) Certification
Author: Etienne Noumen
Publisher: Etienne Noumen
Total Pages: 111
Release:
Genre: Computers
ISBN:
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Welcome to "Ace the AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams"! This book is designed to help you prepare for the AWS Certified Machine Learning - Specialty (MLS-C01) exam and earn your AWS certification. The AWS Certified Machine Learning - Specialty (MLS-C01) exam is designed for individuals who have a strong understanding of machine learning concepts and techniques, and who can design, build, and deploy machine learning models on the AWS platform. In this book, you will find a series of practice exams that are designed to mimic the format and content of the actual MLS-C01 exam. Each practice exam includes a set of multiple choice and multiple response questions that cover a range of topics, including machine learning concepts, techniques, and algorithms, as well as the AWS services and tools used to build and deploy machine learning models. By working through these practice exams, you can test your knowledge, identify areas where you need further study, and gain confidence in your ability to pass the MLS-C01 exam. Whether you are a machine learning professional looking to earn your AWS certification or a student preparing for a career in machine learning, this book is an essential resource for your exam preparation. AWS has created the Certified Machine Learning Specialty (MLS-C01) to assess your ability to identify and solve business problems through machine learning. Passing this exam validates that you have the skills to design, develop, and deploy machine learning models. The AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams will help you prepare for the exam by providing an in-depth review of the exam's content, and by giving you the opportunity to practice your skills. The book covers: Machine Learning Basics and Advanced Concepts via Q&A, Natural Language Processing Quiz, and SageMaker. The Machine Learning Basics and Advanced Concepts section includes questions on topics such as linear regression, decision trees, boosting, Bayesian inference, and deep learning. The Natural Language Processing Quiz covers questions on topics such as part-of-speech tagging, sentiment analysis, and named entity recognition. The SageMaker section includes questions on how to use SageMaker for data pre-processing, model training and tuning, deploying models into a production environment, and troubleshooting. In addition to the basic and advanced machine learning concepts of the practice exams, there is also a section on Exploratory Data Analysis Quiz covering questions on topics such as data visualization, dimensionality reduction techniques, clustering algorithms, and time series analysis. The Modeling Quiz section includes questions on supervised learning algorithms (linear regression, logistic regression,...), unsupervised learning algorithms (k-means clustering,...), reinforcement learning algorithms (Q-learning,...), and dropout methods. Finally, the Machine Learning Implementation and Operations Quiz covers practical questions on topics such as setting up a development environment for machine learning applications, parameter tuning techniques, monitoring machine learning models in production, and handling errors in machine learning applications. Main Topics: Exam Guide AWS Machine Learning Specialty Practice Quiz AWS Machine Learning Specialty Practice Exam I AWS Machine Learning Specialty Practice Exam II AWS Machine Learning Specialty Practice Exam III Basic Machine Learning Concepts Machine Learning Natural Language Processing (NLP) Quiz I Passed AWS Certify Machine Learning Specialty Testimonials Top 10 Technical Insights for Mastering the AWS Certified Machine Learning Specialty Exam in 2023

Learn Amazon SageMaker

Learn Amazon SageMaker
Author: Julien Simon
Publisher: Packt Publishing Ltd
Total Pages: 554
Release: 2021-11-26
Genre: Computers
ISBN: 1801814155
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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.