Dieser Autor taucht in die Bereiche künstliche Intelligenz und maschinelles Lernen ein und kombiniert umfassende praktische Erfahrung mit akademischer Strenge. Durch Lehrtätigkeiten und Beratungen an führenden Universitäten sowie als Gründer eigener KI-Labore gestaltet er aktiv die Zukunft der Technologie mit. Seine Arbeit konzentriert sich darauf, fortgeschrittene KI-Konzepte zugänglich zu machen und ihre praktische Anwendung in der realen Welt zu ermöglichen, wodurch er Lesern wertvolle Einblicke in die Entwicklung und Implementierung cloudbasierter Lösungen und maschinellen Lernens bietet.
Python for DevOps shows you how to harness Python for everyday Linux systems administration tasks, as well as today's most useful devops tools, including Docker, Kubernetes, and Terraform. Embrace automation and you'll never look at a boring task the same way again.
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 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
An Introduction to Cloud-Based Machine Learning
Master powerful off-the-shelf business solutions for AI and machine learning with this practical guide. It helps you tackle real-world challenges using contemporary tools in machine learning, artificial intelligence, and cloud computing, even if you lack a strong math or data science background. The author demystifies essential concepts and highlights powerful cloud offerings from Amazon, Google, and Microsoft, demonstrating effective techniques using the Python data science ecosystem. You'll learn to streamline every step, from deployment to production, creating scalable solutions. As you explore machine learning solutions, you'll develop a deeper understanding of their capabilities and how to maximize their value. The book guides you through building cloud-based AI/ML applications to address practical issues in various fields such as sports marketing, project management, and real estate. Suitable for business professionals, decision-makers, students, and programmers, it offers expert guidance and diverse case studies to help you solve data science problems in any context. You’ll get all the necessary tools, a quick Python review, and insights into the AI and ML toolchain. Additionally, it covers cloud AI solutions with Google Cloud, Amazon Web Services, and Microsoft Azure, and walks you through creating six real-world AI applications from start to finish. Register for convenient access to updates and corrections.