Book Image

Engineering MLOps

By : Emmanuel Raj
Book Image

Engineering MLOps

By: Emmanuel Raj

Overview of this book

Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.
Table of Contents (18 chapters)
1
Section 1: Framework for Building Machine Learning Models
7
Section 2: Deploying Machine Learning Models at Scale
13
Section 3: Monitoring Machine Learning Models in Production

Implementing the Explainable Monitoring framework

To implement the Explainable Monitoring framework, it is worth doing a recap of what has been discussed so far, in terms of implementing hypothetical use cases. Here is a recap of what we did for our use case implementation, including the problem and solution:

  • Problem context: You work as a data scientist in a small team with three other data scientists for a cargo shipping company based in the port of Turku in Finland. 90% of the goods imported into Finland arrive via cargo shipping at various ports across the country. For cargo shipping, weather conditions and logistics can be challenging at times. Rainy conditions can distort operations and logistics at the ports, which can affect supply chain operations. Forecasting rainy conditions in advance allows us to optimize resources such as human resources, logistics, and transport resources for efficient supply chain operations at ports. Business-wise, forecasting rainy conditions...