An engineering discipline aiming to unify Machine Learning systems development (dev) and ML systems deployment (ops) to standardize and streamline the continuous delivery of high-performing models in production is MLOps.
In a wide range of applications today, we are embedding decision automation, which generates a lot of technical challenges that come from building and deploying ML-based systems.
The ML systems lifecycle involves different teams of a data-driven organization.
From start to bottom, the following teams are involved:
- Business development or product team—defining business objectives with KPIs
- Data engineering — data acquisition and preparation
- Data science — architecting ML solutions and developing models
- IT or DevOps—complete deployment setup, monitoring alongside scientists
There are several bottlenecks to be taken care of, and it is not an easy task to manage such systems at scale. The following are the key challenges that the teams have come up with:
- There is a shortage of Data Scientists who are good at developing and deploying scalable web apps. The profile of ML Engineers aims to serve this need. It is at the intersection of Data Science and DevOps.
- Reflecting changing business objectives in the model—with the data continuously changing, maintaining performance standards of the model, and ensuring AI governance, there are many dependencies. It is hard to keep up with the continuous model training and evolving business objectives.
- The communication gaps between technical and business teams with a hard-to-find common language to collaborate. This gap is the reason for the failure of several projects, most often.
- Risk assessment—a lot of debate is doing rounds concerning the black-box nature of ML systems. Often models tend to drift away from what they were initially intended to do. Assessing the risk/cost of such failures is a very important and meticulous step.
Hope this was helpful.