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• 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.