MLOps

Before we start talking about MLOps, let's look at the usual lifecycle in the implementation of an ML model in any organization (I focus on Microsoft technology, but it is not the only one).

MLOps, or Machine Learning Operation, is a growing technology that aims to improve collaboration between data scientists and operations teams. The goal of MLOps is to facilitate the deployment and management of ML models in a production environment. This is important because it helps companies derive value from their machine learning models more quickly and with less risk.

The traditional DevOps model can also be applied to machine learning, but there are some important differences to be aware of. For example, ML models are trained using data, and this data can change over time. This implies that the model must be periodically retrained to maintain its accuracy. In addition, the results of an ML model can be difficult to understand and interpret, which can make it difficult for operations teams to know whether or not the model is performing as expected.

MLOps involves several keys:?

• Version control: As with DevOps, it is important to track changes to the model over time. This helps to ensure that the results of the model can be reproduced and also to understand what has changed in the model if something goes wrong.?
• Continuous integration and continuous delivery: This helps automate the process of creating and deploying machine learning models. Through continuous integration, data scientists can ensure that their code works correctly and that the model is trained correctly. Continuous delivery helps ensure that the model is deployed to production quickly and easily.
• Monitoring and logging: It is important to monitor the performance of the ML model in production to ensure that it is working properly. This involves tracking metrics such as accuracy, as well as logging information about the inputs and outputs of the model. This can quickly identify any problems and understand why the model is making certain predictions.?
• Testing: As in DevOps, it is important to test ML models before putting them into production. This should be done using different test data sets to ensure that the model is working correctly and that it does not overfit the data.
• Model management: Managing the lifecycle of ML models can be complex. It is necessary to track the data that was used to train the model, as well as to track which version of the model is currently deployed in production. In addition, it is important to be able to compare the performance of different models to see which one is performing better.

By following these practices, organizations can improve collaboration between data scientists and operations teams and facilitate the deployment and management of machine learning models in production. This helps reduce the risk of deploying models that do not perform as expected and get value from machine learning models more quickly.

The workflow would be: