Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
€ 65
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Learn the techniques and tools that will cut deployment time from days to minutes
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What Stands Out
Podrobnosti produkta
- 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 format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Pub date | 2020-08-04 |
| Pages | 364 |
| Reading level | Professional & Vocational |
| Subgenre | Computers |
| Edition | 1st Edition |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Binding type | Perfect Binding |
| Assembled product height | 9.19 in (23.3 cm) |
| Assembled product weight | 1.28 lb (580 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to automate and streamline machine learning model workflows using TensorFlow effectively.
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Machine Learning Engineers
Great resource for ML engineers focused on implementing robust pipelines and managing model life cycles efficiently.
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Students and Learners
Beneficial for students eager to understand the practical application of TensorFlow in building machine learning pipelines.
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Complete Beginners
Not suitable for individuals without prior knowledge of machine learning concepts or programming skills.
OPIS IZDELKA
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
Vprašanja in odgovori strank
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Vprašanje:
Who will benefit from this book?
Odgovor: Data scientists, machine learning engineers, and DevOps engineers -
Vprašanje:
What are some of the topics covered in the book?
Odgovor: Machine learning management lifecycle, data pipelines, model analysis, fairness and bias in model performance, privacy-preserving machine learning techniques -
Vprašanje:
What tools can be used for orchestration?
Odgovor: Apache Beam, Apache Airflow, and Kubeflow Pipelines
Hannes Hapke; Catherine Nelson All Books Editorial Review
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Prednosti
- Comprehensive guide on ML pipelines
- Practical examples and projects
- Clear explanations for beginners
- Great for automating processes
- Up-to-date information on TensorFlow
Slabosti
- Some minor typographical errors.
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Značilnosti in prednosti
- 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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