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 Engineering with Python
    sub title: Manage the lifecycle of machine learning models using MLOps with practical examples
  name:
    name part: McMahon, Andrew P.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Polak, Adi
    role:
      role term: contributor.
    role:
      role term: ctb
    role:
      role term: https://id.loc.gov/vocabulary/relators/ctb
  name:
    name part: Knovel (Firm)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (462 pages)
  abstract: Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.What you will learnPlan and manage end-to-end ML developm…
  table of contents: Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples
  note: Andrew P. McMahon.
  note: In English.
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Machine learning
  subject:
    topic: Python (Langage de programmation)
  subject:
    topic: Apprentissage automatique
  subject:
    topic: COMPUTERS / Neural Networks
  subject:
    topic: Neural Networks
  subject:
    topic: Machine Theory
  subject:
    topic: COMPUTERS
  subject:
    topic: Data Modeling & Design
  classification: QA76.73.P98
  classification: 006.3/1
  identifier: 9781837634354
  identifier: 1837634351
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DEGRU
    record creation date: 240723
    record change date: 20260121211150.4
    record identifier: in00005676607
    language of cataloging:
      language term: eng