rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine learning model serving patterns and best practices
    sub title: a definitive guide to deploying, monitoring and providing easy accessibility to machine learning models in production
  name:
    name part: Islam, Md Johirul
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: enk
    place:
      place term: Birmingham
    publisher: Packt Publishing
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key Features Learn best practices about bringing your models to production Explore the tools available for serving ML models and the differences between them Understand state-of-the-art monitoring approaches for model serving implementations Book Description 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. What you will learn Explore specific patterns in model serving that are crucial for every data science professional Understand how to serve machine learning models using different techniques Discover the various approaches…
  note: Md Johirul Islam.
  subject:
    topic: Manufacturing processes
    topic: Data processing
  subject:
    topic: Machine learning
    topic: Industrial applications
  classification: TS183
  classification: 670.285631
  related item:
    identifier: (OCoLC)1348923471
  identifier: 9781803242538
  identifier: 1803242531
  identifier: 1803249900
  identifier: 9781803249902
  identifier: 9781803249902 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 230105
    record change date: 20260128153205.4
    record identifier: in00004673406
    language of cataloging:
      language term: eng