The Little Handbook of MLOps Engineering

When it comes to managing machine learning models, businesses of all kinds are at a crossroads. Many of the machine learning models created exclusively for small scale situations change over time, leaving teams with all sorts of questions. MLOps engineering is a concept that pulls from development operations (DevOps) to streamline automation and trust between teams, creating a robust end-to-end machine learning model life cycle that prioritizes data quality.

In this report, you’ll learn more about the following:

The Machine Learning Lifecycle

The machine learning lifecycle is key in understanding how MLOps engineering processes work. However, the cycle itself has undergone key modifications throughout the years to include specific workflows and other considerations. The best-known version of such a cycle is AWS’ Well-Architected Machine Learning Lifecycle, which introduces best practices that prove handy when implementing an MLOps engineering cycle.

The Fundamentals of MLOps Engineering

The transformative capability of MLOps lies in its openness and flexibility. It’s a mindset that encompasses best practices and tools to monitor machine learning models in real-world production. This section offers a deep dive into the main components of the process, as well as the core principles that guide this framework. From the definition of project objectives to implementation, you’ll understand how your data and processes fit into every single stage.

The Levels of MLOps Engineering

Although many data researchers and data scientists can build effective machine learning models, implementing an MLOps pipeline requires time, resources and knowledge. The creation of different levels of implementation has helped teams and experts easily locate where they are and what they need to improve. This report will help you understand what you need to take your initiative from a level zero, the most basic level within the MLOps pipeline, to a level two.