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35121987
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
Item #:
35121987
GBP 57
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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.
Product Details
- Learn to automate machine learning pipelines using the TensorFlow ecosystem
- Discover techniques to cut deployment time from days to minutes
- Utilize components from TensorFlow Extended to build your pipeline
- Orchestrate your pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines
- Work with data using TensorFlow Data Validation and TensorFlow Transform
- Explore model analysis, fairness, bias, and deployment with TensorFlow tools
| 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?
-
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.
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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.
Product Description
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
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Customer Questions & Answers
-
Question:
Who will benefit from this book?
Answer: Data scientists, machine learning engineers, and DevOps engineers -
Question:
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 -
Question:
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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Pros
- Comprehensive guide on ML pipelines
- Practical examples and projects
- Clear explanations for beginners
- Great for automating processes
- Up-to-date information on TensorFlow
Cons
- Some minor typographical errors.
Product Price History
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GBP 57
Excluding VAT : VAT will be calculated on the checkout page
Order now and get it around Tuesday, July 07
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Secured transaction
Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
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Fastest cross-border delivery
Features & Benefits
- Automate a machine learning pipeline
- Cut deployment time from days to minutes
- Focus on developing new models rather than maintaining legacy systems