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Data Management in Machine Learning Systems (Synthesis Lectures on Data Management)
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Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner.
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Product Details
| Publisher | Springer |
| Publication date | February 25, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 172 pages |
| ISBN-10 | 3031007417 |
| ISBN-13 | 978-3031007415 |
| Item Weight | 11 ounces (311.85 grams) |
| Dimensions | 7.52 x 0.4 x 9.25 inches (19.1 x 1 x 23.5 cm) |
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Data Management in Machine Learning Systems (Synthesis Lectures on Data Management)
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BHD 29
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Features & Benefits
- ML systems underpin modern data-driven applications
- Efficient and scalable execution of ML workloads
- Comprehensive overview of data management in ML systems
- Covers ML support in database systems, DB-inspired ML systems, and ML lifecycle systems
- Addresses optimizing compilers, execution strategies, and resource elasticity
- Serves as a starting point for systems researchers and developers
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