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WNiCF - Interview with Henk - Time series, forecasts and anomaly detections, all hard problems to crack.
Manage episode 470863788 series 3553457
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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.
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