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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

Język publikacji: 1
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 - okladka książki

Autor:
Emmanuel Raj
Serie wydawnicze:
Learning
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
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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