Hitchhiker’s Guide to MLOps for Time Series Forecasting with Sklearn | by Marco Cerliani | Apr, 2024

How to develop a time series forecasting project using Scikit-Learn

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 several businesses. Whether you are working in asset risk management, trading, weather prediction, energy demand forecasting, or traffic analysis, the ability to forecast accurately is crucial for success.

The success of a time series forecasting project is not measured by only the goodness of fit of forecasting models. The effectiveness of an AI-driven tool in practical application also hinges on the level of collaboration among the diverse actors or instruments involved. To grant the smoothest degree of cooperation, a set of rules and best practices must be introduced as soon as possible starting from the initial developing stages.

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…