We discussed the challenges of working with time series data, particularly in the context of machine learning and AI, highlighting the complexity and the need for automation in feature engineering.
The importance of balancing accuracy and complexity in model creation was emphasized, with a focus on avoiding overfitting and ensuring models remain effective in real-world applications.
The potential integration of business context data, such as sales data, with cloud consumption data to enhance anomaly detection and forecasting models was proposed.
The discussion touched on the economic value of anomaly detection, with a focus on proving that early detection can lead to significant cost savings.
The target audience for the anomaly detection system was identified as FinOps managers, who would use the system to manage cloud-related financial topics and coordinate with engineers to address anomalies.

Episode 113: Standing on the shoulders of giants