Autorentext

Noah Gift is a lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years' experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. He is also a certified AWS Solutions Architect. Noah has an MBA from the University of California, Davis; an MS in computer information systems from California State University, Los Angeles; and a BS in nutritional science from Cal Poly, San Luis Obispo. You can find more about Noah by following him on Github (https://github.com/noahgift/), visiting http://noahgift.com, or connecting with him on https://www.linkedin.com/in/noahgift/.

Kennedy Behrman is a veteran consultant specializing in architecting and implementing cloud solutions for early-stage startups. He has both undergraduate and graduate degrees from the University of Pennsylvania, including an MS in Computer Information Technology and post-graduate work in the Computer Graphics and Game Programming program.

He is experienced in data engineering, data science, AWS solutions, and engineering management, and has acted as a technical editor on a number of python and data science-related publications. As a Data Scientist, he helped develop a proprietary growth hacking machine learning algorithm for a startup that led to the exponential growth of the platform. Afterward, he then hired and managed a Data Science team that supported this technology. Additional to that experience, he has been active in the Python language for close to 15 years including giving talks at user groups, writing articles, and serving as technical editor to many publications.

Alfredo Deza is a passionate software engineer, avid open source developer, Vim plugin author, photographer, and former Olympic athlete. He has given several lectures around the world about Open Source Software, personal development, and professional sports. He has rebuilt company infrastructure, designed shared storage, and replaced complex build systems, always in search of efficient and resilient environments. With a strong belief in testing and documentation, he continues to drive robust development practices wherever he is.

As a passionate knowledge-craving developer Alfredo can be found giving presentations in local groups about Python, file systems and storage, system administration, and professional sports.

Grig Gheorghiu has worked for the last 13 years as a programmer, research lab manager, system/network/security architect, and most recently as a software test engineer. Grig is the founder of the Southern California Python Interest Group, as well as a member of the Agile Alliance and of the xpsocal user group. He holds an MS degree in Computer Science from USC. Grig blogs fairly regularly on agile testing topics at agiletesting.blogspot.com.

Klappentext

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 language
• How to automate text, write command-line tools, and automate the filesystem
• Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing
• Cloud computing, infrastructure as code, Kubernetes, and serverless
• Machine learning operations and data engineering from a DevOps perspective
• Building, deploying, and operationalizing a machine learning project