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: Keras to Kubernetes
    sub title: the journey of a machine learning model to production
  name:
    name part: Rao, Dattaraj
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: inu
    date issued: 2019
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (323 pages : : illustrations.)
  abstract: Artificial intelligence (AI) has, in one form or another, been in existence for over six decades. However, recent years have seen an enormous increase in the amount of collectable data and major advancements in algorithms and computer hardware. Within the realm of AI technology, machine learning (ML) and deep learning (DL) applications in particular have undergone significant growth Keras, one of the most popular DL frameworks, can quickly describe a DL model, begin training it on data, and generate more data by modifying existing data. Kubernetes is an application engine that manages applications packaged as containers, handling all the infrastructure constraints such as scaling, fail-over, and load balancing. With the power, flexibility, and virtually limitless applications of Keras and Kubernetes comes a caveat-- they can be challenging to develop and deploy effectively without proper guidance. "Keros to Kubernetes: the journey of a machine learning model to production" offers step-by-step instructions on how to build a Keras model to scale and deploy on a Kubernetes cluster. This timely and accessible guide takes readers through the entire model-to-production process, covering topics such as model serving, scaling, load balancing, API development, algorithm-as-a-service (AaaS), and more. Real-world examples help readers build a Keras model for detecting logos in images, package it was a web application container, and deploy it at scale on a Kubernetes cluster
  table of contents: Big data and artificial intelligence -- Machine learning -- Handling unstructured data -- Deep learning using Keras -- Advanced deep learning -- Cutting-edge deep learning projects -- AI in the modern software world -- Deploying AI models as microservices -- Machine learning development lifecycle -- A platform for machine learning.
  target audience: adult
  note: Dattaraj Rao.
  note: Includes bibliographical references and index.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Big data
  subject:
    topic: Data mining
  subject:
    topic: Machine learning
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Mining
  classification: Q325.5
  classification: 006.312
  identifier: 9781119564843 (electronic bk.)
  identifier: 1119564840 (electronic bk.)
  identifier: 9781119564836
  identifier: 1119564832
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 260209
    record change date: 20260215001055.9
    record identifier: in00005690504
    language of cataloging:
      language term: eng