Unlike what most people think, AI models can be trained with a small number of good examples, in which the target we are looking to detect or predict are solid. What happens in reality is that organizations accumulate tons of data, which is not a bad thing for itself, as you may never know what additional field you are collecting can affect the needs of tomorrow, but they do not always pay attention to the quality of the data.
Add to this edge cases, anomalies that are natural to happen and the result is that the data becomes “noisy” and makes it harder for data experts to strive for good AI models.
Adding more data to the input of the AI pipeline and expecting to get a more accurate model is wrong as well, which leaves the need to dig into the business understanding, get closer to the data operations of the organization, which quickly exceeds the role of the data scientist.
On the other hand, managers, with good knowledge of and experience in the data operations and the meaning behind the collected data, typically do not have algorithmical background and deep math skills.
These MLOps tools are stepping in and help not only the data scientists, but also, help to bridge the gap between them and managers or other matter experts in the organization.
The customization of MLOps tools, help to improve data quality quicker and answers the needs of the organization.
CoreAI team developed a Data-Centric MLOps know-how and expertises in assimilating such approach in organizations, taking the need for research and valuable time of the people involved and offering it as a service .
Becoming Data-Centric, means flexibility in making modifications, filtering and clean up of the data, without the concern of not being able to roll back and examine the success.
Each modification in the data set and labeling is practically creating a new data version and creating a new experiment, together with other variables in an AI pipeline. Experiment management is a fundamental MLOps tool and when set correctly, it becomes the success dashboard that brings the experts in the organization to sit with the data scientists closer.
In practice, a good MLOps platform can help you to accelerate and encourage changes in your data while being able to estimate as a whole organization the success of the AI model, which is the heart of Data-Centric approach.