Cloud Native Machine Learning provides a productivity-boosting alternative to deploying a machine learning model into a production system by using pre-built services from cloud platforms like Azure and AWS. It eliminates the time-consuming operations tasks of setting up and managing custom servers, allowing you to focus on tuning and improving your models.

Format: Paperback / softback
Length: 250 pages
Publication date: 09 March 2022
Publisher: Manning Publications

Deploying Machine Learning Models into Production Systems

The process of deploying a machine learning model into a fully realized production system can be a daunting task, often requiring extensive work by an operations team to create and manage custom servers. This can be a time-consuming and resource-intensive process, especially for organizations that lack the necessary expertise or resources.

Fortunately, there is a solution to this challenge: Cloud Native Machine Learning. Cloud Native Machine Learning leverages the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML systems infrastructure. This approach eliminates the need for organizations to invest in and maintain their own servers, reducing the time and costs associated with infrastructure management.

In this article, we will explore the benefits of Cloud Native Machine Learning and demonstrate how it can help you bridge the gap between experimental machine learning code and production-ready systems. We will also provide a real-world use case for calculating taxi fares, showcasing how to get a serverless ML pipeline up and running using AWS services.

Serverless Machine Learning: A Productivity Boost

One of the key benefits of Cloud Native Machine Learning is its ability to boost productivity. By eliminating the time-consuming operations tasks from your machine learning lifecycle, you can focus on tuning and improving your models without being bogged down by infrastructure management.

Serverless machine learning also offers a high degree of flexibility and scalability. With the serverless capabilities of major cloud vendors handling your infrastructure, you can easily scale your models up or down based on your needs, without the need for physical hardware or complex configuration. This flexibility allows you to adapt to changing business requirements and ensure that your machine learning systems can handle the demands of your users.

Best Practices for Datasets

Before deploying a machine learning model into production, it is essential to ensure that your datasets are of high quality and can be reproduced. Cloud Native Machine Learning provides best practices for dataset management, including principles for data quality and reproducibility.

One of the key principles of data quality is the use of VACUUM data-quality principles. VACUUM ensures that your datasets are clean and consistent, reducing the risk of errors and improving the accuracy of your models. Cloud Native Machine Learning includes tutorials on how to implement VACUUM principles in your projects, ensuring that your datasets can be reproducibly sampled and used for training and testing your machine learning models.

Implementing Machine Learning Models with PyTorch

Cloud Native Machine Learning also provides tutorials on implementing machine learning models with PyTorch, a popular deep learning framework. PyTorch is widely used in the industry for its scalability and flexibility, and Cloud Native Machine Learning helps you leverage these capabilities in your ML systems.

In this tutorial, you will learn how to implement machine learning models with PyTorch, including how to scale up your models in the cloud and how to use PyTorch Lightning for distributed training. You will also learn about best practices for model training, such as using distributed training and optimizing your hyperparameters for better performance.

Conclusion

Deploying a machine learning model into a fully realized production system can be a challenging task, but Cloud Native Machine Learning offers a solution that can help you bridge the gap between experimental code and production-ready systems. By leveraging the pre-built services provided by cloud platforms like Azure and AWS, you can easily assemble your ML systems infrastructure, reduce the time and costs associated with infrastructure management, and focus on tuning and improving your models.

Cloud Native Machine Learning also provides best practices for dataset management, including principles for data quality and reproducibility. With the serverless capabilities of major cloud vendors handling your infrastructure, you can easily scale your models up or down based on your needs, and ensure that your machine learning systems can handle the demands of your users.

In conclusion, Cloud Native Machine Learning is a game-changer for organizations looking to deploy machine learning models into production systems. By leveraging the pre-built services provided by cloud platforms, you can easily assemble your ML systems infrastructure, reduce the time and costs associated with infrastructure management, and focus on tuning and improving your models. Whether you are a startup or a large enterprise, Cloud Native Machine Learning can help you bring your experimental machine learning code to production and achieve your business goals.

Weight: 730g
Dimension: 238 x 186 x 20 (mm)
ISBN-13: 9781617297762