Noah Gift – författare

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

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning

Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science.

Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment.

Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish

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Python is an ideal language for solving problems, especially in Linux and Unix networks. With this pragmatic book, administrators can review various tasks that often occur in the management of these systems, and learn how Python can provide a more efficient and less painful way to handle them.Each chapter in Python for Unix and Linux System Administration presents a particular administrative issue, such as concurrency or data backup, and presents Python solutions through hands-on examples. Once you finish this book, you''ll be able to develop your own set of command-line utilities with Python to tackle a wide range of problems. Discover how this language can help you:

Read text files and extract informationRun tasks concurrently using the threading and forking optionsGet information from one process to another using network facilitiesCreate clickable GUIs to handle large and complex utilitiesMonitor large clusters of machines by interacting with SNMP programmaticallyMaster the IPython Interactive Python shell to replace or augment Bash, Korn, or Z-ShellIntegrate Cloud Computing into your infrastructure, and learn to write a Google App Engine ApplicationSolve unique data backup challenges with customized scriptsInteract with MySQL, SQLite, Oracle, Postgres, Django ORM, and SQLAlchemy

With this book, you''ll learn how to package and deploy your Python applications and libraries, and write code that runs equally well on multiple Unix platforms. You''ll also learn about several Python-related technologies that will make your life much easier.

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you''re trying to crack. This book gives you a head start.

You''ll discover how to:

Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you''re trying to crack. This book gives you a head start.

You''ll discover how to:

Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you''ll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization''s needs.

You''ll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:

Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AIBuild production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning modelsLearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you''ll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization''s needs.

You''ll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:

Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

Python is an ideal language for solving problems, especially in Linux and Unix networks. With this pragmatic book, administrators can review various tasks that often occur in the management of these systems, and learn how Python can provide a more efficient and less painful way to handle them.Each chapter in Python for Unix and Linux System Administration presents a particular administrative issue, such as concurrency or data backup, and presents Python solutions through hands-on examples. Once you finish this book, you''ll be able to develop your own set of command-line utilities with Python to tackle a wide range of problems. Discover how this language can help you:

Read text files and extract informationRun tasks concurrently using the threading and forking optionsGet information from one process to another using network facilitiesCreate clickable GUIs to handle large and complex utilitiesMonitor large clusters of machines by interacting with SNMP programmaticallyMaster the IPython Interactive Python shell to replace or augment Bash, Korn, or Z-ShellIntegrate Cloud Computing into your infrastructure, and learn to write a Google App Engine ApplicationSolve unique data backup challenges with customized scriptsInteract with MySQL, SQLite, Oracle, Postgres, Django ORM, and SQLAlchemy

With this book, you''ll learn how to package and deploy your Python applications and libraries, and write code that runs equally well on multiple Unix platforms. You''ll also learn about several Python-related technologies that will make your life much easier.

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide.

Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide.

Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide.Python foundations, including a brief introduction to the languageHow to automate text, write command-line tools, and automate the filesystemLinux utilities, package management, build systems, monitoring and instrumentation, and automated testingCloud computing, infrastructure as code, Kubernetes, and serverlessMachine learning operations and data engineering from a DevOps perspectiveBuilding, deploying, and operationalizing a machine learning project

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you''re a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.

Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You''ll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you''ll learn how to make your applications more modern, resilient, and cost-effective.

Get started building solutions with C# on AWSLearn DevOps best practices for AWSExplore the development tools and services that AWS providesSuccessfully migrate a legacy .NET application to AWSDevelop serverless .NET microservices on AWSContainerize your .NET applications and move into the cloudMonitor and test your AWS .NET applicationsBuild cloud native solutions that combine the best of the .NET platform and AWS

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you''re a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.

Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You''ll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you''ll learn how to make your applications more modern, resilient, and cost-effective.

Get started building solutions with C# on AWSLearn DevOps best practices for AWSExplore the development tools and services that AWS providesSuccessfully migrate a legacy .NET application to AWSDevelop serverless .NET microservices on AWSContainerize your .NET applications and move into the cloudMonitor and test your AWS .NET applicationsBuild cloud native solutions that combine the best of the .NET platform and AWS