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Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments.
| Publisher | Packt Publishing (November 5, 2021) |
| Language | English |
| Paperback | 276 pages |
| ISBN-10 | 1801079250 |
| ISBN-13 | 978-1801079259 |
| Item Weight | 1.08 pounds (0.49 kg) |
| Dimensions | 9.25 x 7.5 x 0.58 inches (23.5 x 19.1 x 1.5 cm) |
| Country of Origin | This item will be import from US |
| Date First Available | November 14, 2021 |
| What is in the box | Machine Learning Engineering with... For more details, please check description/product details |
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
**** "Machine Learning Engineering with Python" emerges as a pivotal resource for intermediate data scientists and ML engineers seeking a deeper understanding of machine learning implementation in real-world scenarios. Unlike many books that concentrate on theoretical models or isolated ML frameworks, this guide emphasizes practical applications and essential MLops tools that enhance the ability to train, deploy, serve, and iterate on models effectively. The author successfully addresses a significant gap in the understanding of implementation techniques by integrating multiple real-time and batch example scenarios. These practical illustrations not only elucidate critical areas such as versioning, model retraining due to data drift, and automation of hyperparameters, but also dive into deployment and scaling methodologies—particularly noteworthy in chapters on deployment patterns and scaling strategies. Readers have found value in the clarity of explanations, visual aids like diagrams, and organized breakdowns of complex concepts, making it easier to absorb information. Furthermore, the book's repository, offering example datasets and code in Python notebooks, has been a highlight for many, facilitating hands-on learning and practical application. However, some critiques have surfaced regarding the book's focus on AWS for deployment, potentially alienating users of Azure or Google Cloud. Additionally, the end-to-end examples presented may not fully encapsulate the detailed coding necessary for newcomers, suggesting an area for improvement for future editions. Overall, the book serves as an excellent guide into the practical aspects of machine learning engineering, making it a compelling read for professionals eager to enhance their skillset and implement ML solutions in their organizations effectively. **
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