MLOps vs DevOps: Why is MLOps different? (MLOps-2)

MLOPs makes ML development continuous in a never-ending loop

ML operations aim to accelerate, scale and sustain model development, deployment and maintenace.

MLOps is a typical Dev-Ops process, which is the sequence of actions taking an ML project from development to production, except MLOPs has a different nature than Dev-Ops due to the reasons listed below.

1. ML projects are experimental by nature, they require lots of trial and error in addition to testing, validation, integration and deployment before the the project is ready for production. Each of these steps can affect the data or the model in a way that ruins the predictions.
2. ML projects require frequent tuning and retraining due to the rapidly changing nature of data during the development and the runtime as well.
3. Many ML issues cannot be detected or realized until tested in the runtime, which makes the standard quality engineering process not sufficient to ensure the model is working. ML quality process requires continuous evaluation, continuous training and continuous deployment. This requires going through the whole process to deployment every time before we can ensure that the model is ready to be used.
4. ML projects have to be validated ethically before release to ensure that they do not reproduce discriminatory bias or repeat any unethical behaviours that happened in the past.
5. ML models are prone to many run time issues such as anomalies, data drift, concept drift, segment underrepresentation, etc.

ML projects are loopy and reliant on continuously changing data. This requires a highly fluid and responsive operational environment.

The model journey from development to production pass through source control, virtualization, building, testing, issue tracking and deployment. Each of these steps requires some operational task, which is usually performed by a DevOps engineer. This high dependency on DevOps engineers creates an overload on their shoulders and creates a bottleneck in the flow. The sequence diagram below illustrates the journey of the ML model from development to production (figure 1). This sequence diagram shows three problems; (1) The DevOps engineers are overloaded with many tasks to do and the tasks keep repeating in an endless loop, (2) The time…