Do you know how vital MLOps is to your organization?
Nowadays most organizations use Machine Learning (ML) to be competitive and efficient in their industries. However, it is essential to know about better practices when managing ML solutions at the production level. Otherwise, it can be a waste of money as well as time. Simply Machine Learning Operation (MLOps) defines a set of practices for businesses to manage ML projects in an efficient manner.
Also, in the traditional software development environment, a set of practices (DevOps) are used to ship software products into the production environment efficiently and, to run it reliably in real-time. So, the next question comes into our mind is that ‘Why do we not able to use those practices in ML production as well?’ The interesting reason behind that is Data. Since ML models use more data, ML products should be considered at the production level in a slightly different way from DevOps. More simply MLOps is a combination of ML, DevOps and Data Engineering as well.
Why MLOps is important?
ML uses data and never stops changing that data in real-time. Also, it is not possible to control the way data will be changed. When addressing this gap between the code and, the change of data, MLOps is important. Also, monitoring and debugging an ML model manually is a complicated task. As well as time-consuming. So MLOps help with viewing model performance in a centralized way. And it seeks to increase automation and improve the quality of production models. Furthermore, improving the scalability of the products, ease of collaboration, and reproducibility of models and predictions are benefits which can be gained by following MLOps workflow.
MLOps Workflow
The MLOps workflow is often divided into two basic layers. The upper layer is called the pipeline while the lower layer is called the driver. The Pipeline includes Build, Deployment, and Monitoring. And the Driver includes Data, Code, Artifacts, Middleware, and Infrastructure. The following representation will help you to understand better.
Thanks for reading! We will deep dive into each step in the MLOps workflow with my next article.
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