Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (November 5, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 472 pages
ISBN-10 ‏ : ‎ 1098156013
ISBN-13 ‏ : ‎ 978-1098156015
Item Weight ‏ : ‎ 1.65 pounds
Dimensions ‏ : ‎ 7 x 0.95 x 9.19 inches

Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

In today’s data-driven world, machine learning has become a crucial tool for businesses looking to gain insights and make better decisions. However, building and deploying machine learning models in production can be a complex and challenging process.

Machine learning production systems are responsible for the end-to-end process of developing, deploying, and maintaining machine learning models in a production environment. This involves not only building and training models, but also managing data pipelines, monitoring model performance, and ensuring that models are deployed and scaled effectively.

One key component of machine learning production systems is the engineering of machine learning models and pipelines. This involves designing and implementing the architecture and infrastructure needed to train, test, and deploy machine learning models at scale.

Engineers working on machine learning production systems need to have a deep understanding of machine learning algorithms, as well as experience in software engineering and data infrastructure. They must be able to design and implement robust, scalable machine learning pipelines that can handle large volumes of data and be deployed in a production environment.

In addition, engineers working on machine learning production systems need to have a strong understanding of best practices for model evaluation, monitoring, and maintenance. They must be able to ensure that models are performing as expected, and be able to quickly identify and address any issues that arise.

Overall, building and deploying machine learning models in production requires a combination of technical skills, domain knowledge, and a deep understanding of machine learning algorithms and best practices. By engineering robust machine learning models and pipelines, businesses can harness the power of machine learning to drive better decision-making and gain a competitive edge in today’s data-driven world.
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