The need for MLOps
The journey from AI research to production is long and full of hurdles. The complete AI/ML workload, whether building models, deploying models, or allocating web resources, is cumbersome, as any change in one step leads to changes in another. Even with advancements in deep learning, the process of taking an idea to production can be pretty lengthy.
Figure 6.7 shows the different components of an ML system. We can see that only a small fraction of an ML system is involved in the actual learning and prediction; however, it requires the support of a vast and complex infrastructure. The problem is aggravated by the fact that Changing Anything Changes Everything (CACE), such that minorly tweaking the hyperparameters, changing the learning settings, or modifying the data selection methods can mean that the whole system needs to change:
Figure 6.7 – The different components of an ML system
In the IT sector, speed, reliability, and...