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

Computers & Technology

Gift, Noah

Computer Science

Addison-Wesley Professional

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: 
<ul>
<li>Read text files and extract information</li>
<li>Run tasks concurrently using the threading and forking options</li>
<li>Get information from one process to another using network facilities</li>
<li>Create clickable GUIs to handle large and complex utilities</li>
<li>Monitor large clusters of machines by interacting with SNMP programmatically</li>
<li>Master the IPython Interactive Python shell to replace or augment Bash, Korn, or Z-Shell</li>
<li>Integrate Cloud Computing into your infrastructure, and learn to write a Google App Engine Application</li>
<li>Solve unique data backup challenges with customized scripts</li>
<li>Interact with MySQL, SQLite, Oracle, Postgres, Django ORM, and SQLAlchemy</li>
</ul>
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.

Python for Unix and Linux System Administration

Jones, Jeremy M.

Web Development & Design

O'Reilly Media

Product Description \nGetting 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:\nApply DevOps best practices to machine learning 
Build production machine learning systems and maintain them 
Monitor, instrument, load-test, and operationalize machine learning systems 
Choose the correct MLOps tools for a given machine learning task 
Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware\nAbout the Author 
Noah Gift is the founder of Pragmatic A.I. Labs. He lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative, and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, AI, and data science courses, and consulting on machine learning and cloud architecture for students and faculty. As a former CTO, individual contributor, and consultant he has over 20 years' experience shipping revenue-generating products in many industries including film, games, and SaaS.\nAlfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete with almost two decades of DevOps and software engineering experience. He currently teaches Machine Learning Engineering and gives worldwide lectures about software development, personal development, and professional sports. Alfredo has written several books about DevOps and Python, and continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.

Practical MLOps: Operationalizing Machine Learning Models

Deza, Alfredo

Implementing MLOps in the Enterprise: A Production-First Approach

Haviv, Yaron

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.
<ul>
<li>Python foundations, including a brief introduction to the language</li>
<li>How to automate text, write command-line tools, and automate the filesystem</li>
<li>Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing</li>
<li>Cloud computing, infrastructure as code, Kubernetes, and serverless</li>
<li>Machine learning operations and data engineering from a DevOps perspective</li>
<li>Building, deploying, and operationalizing a machine learning project</li>
</ul>

Python for DevOps: Learn Ruthlessly Effective Automation

Behrman, Kennedy

Gheorghiu, Grig

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 AWS Learn DevOps best practices for AWS Explore the development tools and services that AWS provides Successfully migrate a legacy .NET application to AWS Develop serverless .NET microservices on AWS Containerize your .NET applications and move into the cloud Monitor and test your AWS .NET applications Build cloud native solutions that combine the best of the .NET platform and AWS

Developing on AWS with C#: A Comprehensive Guide on Using C# to Build Solutions on the AWS Platform

Charlesworth, James

Networking & Cloud Computing

Testing In Python: Robust Testing For Professionals

Independently published

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