Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
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Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
Learn the techniques and tools that will cut deployment time from days to minutes
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
Elementas #: 35121987

Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback

Elementas #: 35121987

€ 65

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What Stands Out

Comprehensive Guidance
Offers in-depth tutorials on automating the machine learning lifecycle, making it accessible for both beginners and experienced practitioners seeking to enhance their workflows.
Hands-On Projects
Includes practical projects that demonstrate real-world applications of TensorFlow, fostering hands-on learning and improving retention of complex concepts essential for mastering ML pipelines.
Expert Insights
Features contributions from industry experts, providing invaluable insights and best practices that help users avoid common pitfalls and enhance their machine learning implementations.

Produkto Aprašymas

Discover the best guide to automate your model life cycles with Tensorflow. Get the paperback edition of Building Machine Learning Pipelines by Hannes Hapke and Catherine Nelson. Order now online at Ubuy Lithuania.
  • Automate machine learning pipeline using TensorFlow ecosystem
  • Cut deployment time from days to minutes
  • Utilize components from TensorFlow Extended, Apache Beam, Airflow, and Kubeflow Pipelines
  • Work with data using TensorFlow Data Validation and Transform
  • Analyze model in detail with TensorFlow Model Analysis, examine fairness and bias
  • Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices, learn privacy-preserving techniques
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Pub date2020-08-04
Pages364
Reading levelProfessional & Vocational
SubgenreComputers
Edition1st Edition
PublisherO'Reilly Media
Original languagesEnglish
LanguageEnglish
Binding typePerfect Binding
Assembled product height9.19 in (23.3 cm)
Assembled product weight1.28 lb (580 grams)
Bisac subject headingComputers

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists seeking to automate and streamline machine learning model workflows using TensorFlow effectively.

  • Machine Learning Engineers

    Great resource for ML engineers focused on implementing robust pipelines and managing model life cycles efficiently.

  • Students and Learners

    Beneficial for students eager to understand the practical application of TensorFlow in building machine learning pipelines.

Not Suitable For
  • Complete Beginners

    Not suitable for individuals without prior knowledge of machine learning concepts or programming skills.

  • Casual Readers

    May not engage casual readers looking for non-technical literature or general overviews in machine learning.

  • Business Executives

    Less useful for executives seeking high-level strategies rather than technical insights on model development.

PRODUKTO APRAŠYMAS

Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback

Turite klausimų? Kalbekites su mumis

Klientų klausimai ir atsakymai

  • klausimas: Who will benefit from this book?

    Answer: Data scientists, machine learning engineers, and DevOps engineers
  • klausimas: What are some of the topics covered in the book?

    Answer: Machine learning management lifecycle, data pipelines, model analysis, fairness and bias in model performance, privacy-preserving machine learning techniques
  • klausimas: What tools can be used for orchestration?

    Answer: Apache Beam, Apache Airflow, and Kubeflow Pipelines

Hannes Hapke; Catherine Nelson All Books Editorial Review

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€ 65

Užsisakykite dabar ir gaukite jį Šeštadienis, Liepa 18

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Savybės ir privalumai

  • Automate a machine learning pipeline
  • Cut deployment time from days to minutes
  • Focus on developing new models rather than maintaining legacy systems

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