Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition

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Autor:: Emmanuel Raj

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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition Emmanuel Raj - okladka książki

Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition Emmanuel Raj - audiobook MP3

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Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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Getting machine learning (ML) models into production continues to remain challenging using traditional software development methods. This book highlights the changing trends of software development over time and solves the problem of integrating ML with traditional software using MLOps.
In this new edition of Engineering MLOps, Emmanuel Raj demystifies MLOps to equip you with the skills needed to build your own MLOps pipelines using -of-the-art tools (MLFlow, DVC, KubeFlow, Locust.io, Docker, Kubernetes, Apache Spark, to name a few) and platforms. You will start by learning the essentials of ML engineering and build ML pipelines to train and deploy models. The book then covers how to implement an MLOps solution for a real-life business problem using Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), as well as cloud agnostic tools. You'll also understand how to build continuous integration/deployment (CI/CD) and continuous delivery pipelines to build, test, deploy, and monitor your models.
By the end of the book, you will become proficient at building, deploying, and monitoring any ML model with the MLOps process using any tool or platform.

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O autorze książki

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.

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Oceny i opinie klientów: Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition Emmanuel Raj (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

Tytuł oryginału:: Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition
ISBN Ebooka:: 978-18-032-4930-8, 9781803249308
Data wydania ebooka :: 2025-09-19 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1

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Kategorie:
Programowanie » Python - Programowanie
Programowanie » Programowanie w chmurze

Spis treści książki

1. Introduction to MLOps
2. MLOps for Business
3. Basics of ML Engineering
4. Characterizing your Machine Learning Problem for MLOps
5. Machine Learning Pipelines
6. Model Evaluation and Packaging
7. Deploying your models as a batch or live endpoint
8. Deploying your models as a streaming service
9. Building robust CI and CD pipelines
10. API and microservice Management
11. Essentials of testing release
12. Essentials of production release
13. Key principles for monitoring your ML system
14. Model serving and metrics selection
15. Continuous delivery and continuous monitoring
16. Orchestrating ML pipelines in Azure Synapse
17. Governance of ML Solutions
18. Building Machine Learning Models
19. Deploying Machine Learning Models
21. MLOps Inspiration and Use Cases
22. Building a Technical Portfolio for MLOps
23. Monitoring and Governance

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