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    Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production Md Johirul Islam

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production Md Johirul Islam - okladka książki

    Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production Md Johirul Islam - okladka książki

    Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production Md Johirul Islam - audiobook MP3

    Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production Md Johirul Islam - audiobook CD

    Autor:
    Md Johirul Islam
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    336
    Dostępne formaty:
         PDF
         ePub
    Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.
    This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you’ll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples.
    By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques.

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    O autorze książki

    Md Johirul Islam is a Data Scientist and Machine Learning Researcher at AWS. He has a PhD in Computer Science and is also an adjunct professor at Purdue University. His expertise are focused on designing explainable, maintainable and robust data science pipeline applying the software design principles and helping organizations deploy Machine Learning models into production at Scale.

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