In the world of data-driven decision making, time series forecasting plays a pivotal role by leveraging historical data patterns to predict future outcomes for multiple businesses. Whether you work in asset risk management, trading, weather forecasting, energy demand forecasting or traffic analysis, the ability to forecast accurately is crucial to success.
The success of a time series forecasting project is not measured solely by the quality of fit of the forecast models. The practical effectiveness of an AI-based tool also depends on the level of collaboration between the various actors or instruments involved. To ensure the smoothest possible degree of cooperation, a set of rules and best practices should be introduced as early as possible, from the earliest stages of development.
These rules are known as machine learning operations (MLOps).
MLOps serves to unify various elements of an ML project into a singular, harmonious structure striving to maintain this seamless integration and functionality in the future.
The concept of MLOps is not new in the software development community. It inherits from the DevOps approach the standard phases of a software product release such as Continuous integration (ensure normal operation while allowing automatic updates) And Continuous delivery (automatic deployment); while introducing some specific phases specific to Machine Learning such as Continuing Education (automatic feature engineering, parameter tuning and retraining) And Continuous monitoring (automatic monitoring of performance and data drift).
Nowadays, there are many tools and frameworks claiming to solve the shortcomings of MLOps. Indeed, there is no single method or architecture for solving MLOps problems. The choices are linked to the task to be carried out. In the case of time series forecasting, it is important to focus on the temporal dependence present in the data in both the development and operation phases.