First, Artificial Intelligence and Machine Learning (AI/ML) capabilities need to be added to the data flow. Typical data pipelines and database tools can perform data transformations, but do not have AI capabilities.
Second, a well-performing machine learning model needs to be generated in a short amount of time. In a traditional machine learning model training approach, one must carry out multiple rounds of data pre-processing, hyperparameter tuning, and optimization in order to identify the best-performing model. This requires time and compute resources which we did not have in a hackathon setting.
Third, is the maintainability of the machine learning model. After the ML model has been trained and deployed, it needs to be ran regularly to predict customer behaviors. As more recent data is collected, the model also needs to be retrained on a regular basis to accommodate changes. Thus, a fully automated machine learning model training and scoring system is needed.