chapter two

2 What is MLOps ?

 

This chapter covers

  • Understanding MLOps and its role in production ML
  • Key challenges in building reliable ML systems
  • How MLOps differs from traditional DevOps
  • Building confidence through structured ML processes

In Chapter 1, we introduced the ML lifecycle and the foundational skills needed to become an effective ML Engineer. Now, let's dig deeper into MLOps - the practices and principles that will help you reliably deliver value through ML systems. Machine learning and the models are often not the end product of an organization, but rather a means to an end.

The gap between business value generation, requirements and necessary infrastructure is the primary reason why ML and by extension MLOps is hard. Very few companies truly do research on model development and instead reuse architectures and train/adapt off the shelf models for specific domains and problem sets. Availability of comprehensive open source libraries like Huggingface also potentially make modeling trivial. After defining a problem and identifying an architecture to solve the problem statement, the hard questions come into focus. How would the model be trained? How would data get to the model, how would it interact with the other services, where would it be run and how do we make sure the model is accurate over time?

2.1 The iterative MLOps lifecycle

2.1.1 Data collection

2.1.2 Exploratory Data Analysis (EDA)

2.1.3 Modelling and Training

2.1.4 Model Evaluation

2.1.5 Deployment

2.1.6 Monitoring

2.1.7 Maintenance, updates and review

2.2 Why is robust MLOps important ?

2.3 Role of MLOps in a mature organization

2.4 DevOps vs MLOps

2.5 Levels of MLOps maturity

2.5.1 Level 0 - Basic

2.5.2 Level 1 - Intermediate

2.5.3 Level 2 - Advanced

2.6 Summary