  This paper presents DeepTSF, a comprehensive machine learning operations
(MLOps) framework aiming to innovate time series forecasting through workflow
automation and codeless modeling. DeepTSF automates key aspects of the ML
lifecycle, making it an ideal tool for data scientists and MLops engineers
engaged in machine learning (ML) and deep learning (DL)-based forecasting.
DeepTSF empowers users with a robust and user-friendly solution, while it is
designed to seamlessly integrate with existing data analysis workflows,
providing enhanced productivity and compatibility. The framework offers a
front-end user interface (UI) suitable for data scientists, as well as other
higher-level stakeholders, enabling comprehensive understanding through
insightful visualizations and evaluation metrics. DeepTSF also prioritizes
security through identity management and access authorization mechanisms. The
application of DeepTSF in real-life use cases of the I-NERGY project has
already proven DeepTSF's efficacy in DL-based load forecasting, showcasing its
significant added value in the electrical power and energy systems domain.
