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

Machine learning training and hyperparameter optimization

We are all set to do the fun part, training ML models! This step enables model training; it has modular scripts or code that perform all the traditional steps in ML training, such as fitting and transforming data to train the model and hyperparameter tuning to converge the best model. The output of this step is a trained ML model.

To solve the business problem, we will train two well-known models using the Support Vector Machine classifier and the Random Forest classifier. These are chosen based on their popularity and consistency of results; you are free to choose models of your choice – there are no limitations in this step. First, we will train the Support Vector Machine classifier and then the Random Forest classifier.

Support Vector Machine

Support Vector Machine (SVM) is a popular supervised learning algorithm (used for classification and regression). The data points are classified using hyperplanes in...