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

Toward the ML Pipeline

So far, we have processed the data by working on irregularities such as missing data, selected features by observing correlations, created new features, and finally ingested and versioned the processed data to the Machine learning workspace. There are two ways to fuel the data ingestion for ML model training in the ML pipeline. One way is from the central storage (where all your raw data is stored) and the second way is using a feature store. As knowledge is power, Let's get to know the use of the feature store before we move to the ML pipeline.

Feature Store

A feature store compliments the central storage by storing important features and make them available for training or inference. A feature store is a store where you transform raw data into useful features that ML models can use directly to train and infer to make predictions. Raw Data typically comes from various data sources, which are structured, unstructured, streaming, batch, and real-time...