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

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autor:
    Emmanuel Raj
    Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale Emmanuel Raj - okladka książki

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

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

    Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale Emmanuel Raj - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    370
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.

    The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.

    By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

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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.

    Packt Publishing - inne książki

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