Functions of Judges in the Future Some
Posted: Tue Jan 21, 2025 10:44 am
Today, ML specialists are involved at all stages of the project: this includes project initiation, the data preparation stage, the experiment, the operation stage, and further monitoring and retraining of models. Since we are talking about the fintech sphere, one of the important advantages now is how quickly the system adapts to external conditions and how quickly the team adjusts processes so that the models work effectively.
Due to the regulatory specifics of the sector, we bahamas telegram database gradually came to the conclusion that the best solution for us would be our own infrastructure (MLOPs platform), which will allow us to work more flexibly on improving and automatically retraining all the interconnected services in our large list: from anti-fraud models and debt load calculation models to a number of technical services.
Right now, we are working on bringing our idea to life. On the topic: Are IT specialists now humanities scholars? How AI has changed the agency market How to implement ML services in a company: practical advice First of all, it is necessary to understand that each company that wants to implement ML services in its work may be at a different starting point: at a different stage of maturity of the team and business processes.
The first step for any organization interested in ML systems is to conduct an internal audit and determine the level of readiness for the implementation of innovations in processes. Tip: To determine where you stand, a company can use the MLOPs maturity scale previously proposed by Google .
In short, there are only five maturity levels: Manual - when all data processing models are created and trained manually by engineers. Repeatable - when repositories appear in the system. Reproducible - there is a feature store and model repository in place. Automated - A/B testing of the current model and the new model appears.
Continuously improving - the system re-learns automatically when triggers appear from the monitoring system. Depending on which point on the scale the situation in the company corresponds to, the further development trajectory is determined and, accordingly, resources are allocated. For example, to find an MLOps engineer or create a repository.
Due to the regulatory specifics of the sector, we bahamas telegram database gradually came to the conclusion that the best solution for us would be our own infrastructure (MLOPs platform), which will allow us to work more flexibly on improving and automatically retraining all the interconnected services in our large list: from anti-fraud models and debt load calculation models to a number of technical services.
Right now, we are working on bringing our idea to life. On the topic: Are IT specialists now humanities scholars? How AI has changed the agency market How to implement ML services in a company: practical advice First of all, it is necessary to understand that each company that wants to implement ML services in its work may be at a different starting point: at a different stage of maturity of the team and business processes.
The first step for any organization interested in ML systems is to conduct an internal audit and determine the level of readiness for the implementation of innovations in processes. Tip: To determine where you stand, a company can use the MLOPs maturity scale previously proposed by Google .
In short, there are only five maturity levels: Manual - when all data processing models are created and trained manually by engineers. Repeatable - when repositories appear in the system. Reproducible - there is a feature store and model repository in place. Automated - A/B testing of the current model and the new model appears.
Continuously improving - the system re-learns automatically when triggers appear from the monitoring system. Depending on which point on the scale the situation in the company corresponds to, the further development trajectory is determined and, accordingly, resources are allocated. For example, to find an MLOps engineer or create a repository.