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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
A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.
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.
Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book DescriptionData wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
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
Master the Fundamentals of Data Analytics at Scale KEY FEATURES ● Comprehensive guide to constructing data engineering workflows spanning diverse data sources ● Expert techniques for transforming and visualizing data to extract actionable insights ● Advanced methodologies for analyzing data and employing machine learning to uncover intricate patterns DESCRIPTION Embark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook. Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets. The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons. The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence. WHAT WILL YOU LEARN ● Construct streamlined data engineering workflows capable of ingesting data from diverse sources and formats. ● Employ data transformation tools to efficiently cleanse and reshape data, priming it for analysis. ● Perform ad-hoc queries for preliminary data exploration, uncovering initial insights. ● Utilize prepared datasets to craft compelling, interactive data visualizations that communicate actionable insights. ● Develop advanced machine learning and Generative AI workflows to delve into intricate aspects of complex datasets, uncovering deeper insights. WHO IS THIS BOOK FOR? This book is ideal for aspiring data engineers, analysts, and data scientists seeking to deepen their understanding and practical skills in data engineering, data transformation, visualization, and advanced analytics. It is also beneficial for professionals and students looking to leverage AWS services for their data-related tasks. TABLE OF CONTENTS 1. Introduction to Data Analytics and AWS 2. Getting Started with AWS 3. Collecting Data with AWS 4. Processing Data on AWS 5. Descriptive Analytics on AWS 6. Advanced Data Analysis on AWS 7. Additional Use Cases for Data Analysis 8. Data Visualization and Interaction on AWS 9. The Future of Data Analytics 10. Conclusion and Next Steps Index
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)
Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you'll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python.
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key FeaturesBuild data pipelines that require distributed processing capabilities on a large volume of dataDiscover the security features of EMR such as data protection and granular permission managementExplore best practices and optimization techniques for building data analytics solutions in Amazon EMRBook Description Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learnExplore Amazon EMR features, architecture, Hadoop interfaces, and EMR StudioConfigure, deploy, and orchestrate Hadoop or Spark jobs in productionImplement the security, data governance, and monitoring capabilities of EMRBuild applications for batch and real-time streaming data analytics solutionsPerform interactive development with a persistent EMR cluster and NotebookOrchestrate an EMR Spark job using AWS Step Functions and Apache AirflowWho this book is for This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
Covers Data Science concepts, processes, and the real-world hands-on use cases. KEY FEATURES ● Covers the journey from a basic programmer to an effective Data Science developer. ● Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ● Implementation of MLOps using Microsoft Azure DevOps. DESCRIPTION "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. WHAT YOU WILL LEARN ● Organize Data Science projects using CRISP-DM and Microsoft TDSP. ● Learn to acquire and explore data using Python visualizations. ● Get well versed with the implementation of data pre-processing and Feature Engineering. ● Understand algorithm selection, model development, and model evaluation. ● Hands-on with Azure ML Service, its architecture, and capabilities. ● Learn to use Azure ML SDK and MLOps for implementing real-world use cases. WHO THIS BOOK IS FOR This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. TABLE OF CONTENTS 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models