In recent times, machine learning has evolved quickly and now it is playing a crucial role in many different fields. To get the best out of machine learning, one needs to put them in the production pipeline but due to this, we have to face a lot of challenges, especially when it is done on a large scale. These challenges can’t be completely managed by DevOps practices because machine learning difficulties are different. That’s when MLOps (Machine Learning Operations) come into play. With our analysis on ML Ops, we come to know that there are three interconnected key elements of it and they are namely machine learning , Dev Ops and data engineering.
We consider data as one of the most important tools in business that is directly or indirectly related to how an organization is going to adapt to future systems. ML Ops is a process in which one takes both data and code to predict which deployment should be put on a large production. This process needs operations(code) and data engineering(data) teams to work together for smooth functioning. We are going to discuss the benefits of ML Ops, as their impact on life is immense.
Benefits of ML Ops:
It helps in enhancing user experience due to the fact that data keeps on updating timely. It plays an important role in updating the machine learning model. Data scientists do not need to keep worrying about updating data instead they can completely focus on model performance.
It brings technology, people, and processes together to resolve the issue related to different entities of a machine learning project which are not easy to manage by DevOps.
It provides model governance solutions that become important in big organizations where models are deployed on a huge scale and manual tracking of rules and regulations become almost impossible.
It helps to maintain the health of the machine learning model at a large scale. Sustaining the health of such models is important. Deployed models need to be continuously monitored for their behaviour on real-world data and problems like dada drift, model drift, and many more should be detected by them automatically.
It provides Continuous integration and continuous deployment (CICD) for machine learning projects. DevOps also built CICD for software products but it is not useful for machine learning products. This increases the need for ML Ops throughout the world.
This article is an attempt to cover almost all the aspects of ML Ops. Apart from it, there are other Model Ops which are mainly focused on the governance and life cycle management of almost all the AI models. It is a bit different from ML Ops as the main function of ML Ops is to focus only on the functioning of machine learning models. From our discussion, we conclude that importance of ML Ops in our life cannot be neglected, because of the task it performs. The world is heading towards the use of artificial intelligence and machine learning at a fast rate. Use of it can be seen in almost every field. With the increase in the use of AI and ML, the importance of ML Ops also increases rapidly.