Have you ever heard of the term MLOps? If you have been working in the field of AI or ML, I am sure you must have come across this term somewhere. But if the answer is still a no, this article will help you understand what we mean by the word ‘MLOps’.
‘DevOps’ is a very famous term. I am sure you know what that means. But if you are new to the field of software development, I will brief it out now. DevOps consists of the processes that combine software development and IT operations. In simple terms, they are the operations required in the development of softwares. But note that it is a process and not a product.
DevOps was a massive boon to the software industry. It helped engineers facilitate the process of software development and its related operations.
That was when ML Engineers and Data Scientists thought of applying DevOps techniques to machine learning, and they coined the term ‘MLOps’. MLOps is a machine learning culture that blends ML system development with machine learning system operations and data engineering. In short, it is a combination of Development and Operations in the field of ML.
In developing an ML model, only a small fraction of the whole process contains ML code. The rest of the process include Data collection, Testing, and Debugging, Process Management, Feature Engineering, Model Analysis, etc. All these sub-processes are part of DevOps culture. And Data Engineering helps in the production process of an ML model.
There is a fundamental difference between machine learning and normal software development comes. While following DevOps principles did help in an ML process, DevOps is more leaned towards software development operations.
ML is not just a code; it is a combination of code and data. And for this same reason, applying DevOps in ML is slightly different even though most of the sub-processes remain the same. Although the ML system is a software system, the development team does not consist of software engineers. The ML Engineers and Data Scientists focus on exploratory data analysis and experimentation and not world-class production.
Another reason for the formation of MLOps was because ML is experimental in nature, while software development is straightforward. One has to try different algorithms, process models, and data to make a complete ML model. Testing an ML model is very different. While typical unit and integrity testings are done, data validation, trained model quality evaluation, and model valuation also occur.
ML requires the deployment of multi-step pipelines to retrain and deploy the model automatically. This only adds complexity to the code and the model. The production has low performance, as the data gets varied each and every day. The model can decay very fastly compared to regular softwares due to different reasons, and therefore summary statistics should be kept to update the model.
Although unit testing, continuous integration, and delivery of an ML system are similar to other software systems, there are some notable differences between them.
MLOps is a relatively new field. There is a common belief that by 2030 the world would fully incorporate artificial intelligence into daily life. There is a huge requirement for AI, ML, and Data Scientists in today’s scientific and corporate fields. Once the advantages of MLOps are thoroughly studied, there will be an excellent demand for MLOps Engineers.